TY - JOUR AU - Yanagisawa, Yuki AU - Watabe, Satoshi AU - Yokoyama, Sakura AU - Sayama, Kyoko AU - Kizaki, Hayato AU - Tsuchiya, Masami AU - Imai, Shungo AU - Someya, Mitsuhiro AU - Taniguchi, Ryoo AU - Yada, Shuntaro AU - Aramaki, Eiji AU - Hori, Satoko PY - 2025/3/11 TI - Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition JO - JMIR Cancer SP - e69663 VL - 11 KW - natural language processing KW - pharmaceutical care records KW - androgen receptor axis-targeting agents KW - adverse events KW - outpatient care N2 - Background: Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists. Objective: This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records. Methods: We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs. Results: The F1-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8%) were positive symptom tags (symptoms present), while 1067 tags (56.2%) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as ?pain,? ?skin disorders,? ?fatigue,? and ?gastrointestinal symptoms.? Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists? AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition. Conclusions: The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management. UR - https://cancer.jmir.org/2025/1/e69663 UR - http://dx.doi.org/10.2196/69663 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/69663 ER - TY - JOUR AU - Zhong, Jinjia AU - Zhu, Ting AU - Huang, Yafang PY - 2025/2/25 TI - Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study JO - J Med Internet Res SP - e56774 VL - 27 KW - artificial intelligence KW - randomized controlled trial KW - reporting quality KW - primary care KW - meta-epidemiological study N2 - Background: The surge in artificial intelligence (AI) interventions in primary care trials lacks a study on reporting quality. Objective: This study aimed to systematically evaluate the reporting quality of both published randomized controlled trials (RCTs) and protocols for RCTs that investigated AI interventions in primary care. Methods: PubMed, Embase, Cochrane Library, MEDLINE, Web of Science, and CINAHL databases were searched for RCTs and protocols on AI interventions in primary care until November 2024. Eligible studies were published RCTs or full protocols for RCTs exploring AI interventions in primary care. The reporting quality was assessed using CONSORT-AI (Consolidated Standards of Reporting Trials?Artificial Intelligence) and SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials?Artificial Intelligence) checklists, focusing on AI intervention?related items. Results: A total of 11,711 records were identified. In total, 19 published RCTs and 21 RCT protocols for 35 trials were included. The overall proportion of adequately reported items was 65% (172/266; 95% CI 59%-70%) and 68% (214/315; 95% CI 62%-73%) for RCTs and protocols, respectively. The percentage of RCTs and protocols that reported a specific item ranged from 11% (2/19) to 100% (19/19) and from 10% (2/21) to 100% (21/21), respectively. The reporting of both RCTs and protocols exhibited similar characteristics and trends. They both lack transparency and completeness, which can be summarized in three aspects: without providing adequate information regarding the input data, without mentioning the methods for identifying and analyzing performance errors, and without stating whether and how the AI intervention and its code can be accessed. Conclusions: The reporting quality could be improved in both RCTs and protocols. This study helps promote the transparent and complete reporting of trials with AI interventions in primary care. UR - https://www.jmir.org/2025/1/e56774 UR - http://dx.doi.org/10.2196/56774 UR - http://www.ncbi.nlm.nih.gov/pubmed/39998876 ID - info:doi/10.2196/56774 ER - TY - JOUR AU - Ahn, Seong-Ho AU - Yim, Kwangil AU - Won, Hyun-Sik AU - Kim, Kang-Min AU - Jeong, Dong-Hwa PY - 2024/12/16 TI - Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study JO - J Med Internet Res SP - e63476 VL - 26 KW - COVID-19 KW - confirmed case prediction KW - search engine queries KW - query expansion KW - word embedding KW - public health KW - case prediction KW - South Korea KW - search engine KW - infodemiology KW - infodemiology study KW - policy KW - lifestyle KW - machine learning KW - machine learning techniques KW - utilization KW - temporal variation KW - novel framework KW - temporal KW - web-based search KW - temporal semantics KW - prediction model KW - model N2 - Background: The number of confirmed COVID-19 cases is a crucial indicator of policies and lifestyles. Previous studies have attempted to forecast cases using machine learning techniques that use a previous number of case counts and search engine queries predetermined by experts. However, they have limitations in reflecting temporal variations in queries associated with pandemic dynamics. Objective: This study aims to propose a novel framework to extract keywords highly associated with COVID-19, considering their temporal occurrence. We aim to extract relevant keywords based on pandemic variations using query expansion. Additionally, we examine time-delayed web-based search behavior related to public interest in COVID-19 and adjust for better prediction performance. Methods: To capture temporal semantics regarding COVID-19, word embedding models were trained on a news corpus, and the top 100 words related to ?Corona? were extracted over 4-month windows. Time-lagged cross-correlation was applied to select optimal time lags correlated to confirmed cases from the expanded queries. Subsequently, ElasticNet regression models were trained after reducing the feature dimensions using principal component analysis of the time-lagged features to predict future daily case counts. Results: Our approach successfully extracted relevant keywords depending on the pandemic phase, encompassing keywords directly related to COVID-19, such as its symptoms, and its societal impact. Specifically, during the first outbreak, keywords directly linked to COVID-19 and past infectious disease outbreaks similar to those of COVID-19 exhibited a high positive correlation. In the second phase of the pandemic, as community infections emerged, keywords related to the government?s pandemic control policies were frequently observed with a high positive correlation. In the third phase of the pandemic, during the delta variant outbreak, keywords such as ?economic crisis? and ?anxiety? appeared, reflecting public fatigue. Consequently, prediction models trained by the extracted queries over 4-month windows outperformed previous methods for most predictions 1-14 days ahead. Notably, our approach showed significantly higher Pearson correlation coefficients than models based solely on the number of past cases for predictions 9-11 days ahead (P=.02, P<.01, and P<.01), in contrast to heuristic- and symptom-based query sets. Conclusions: This study proposes a novel COVID-19 case-prediction model that automatically extracts relevant queries over time using word embedding. The model outperformed previous methods that relied on static symptom-based or heuristic queries, even without prior expert knowledge. The results demonstrate the capability of our approach to track temporal shifts in public interest regarding changes in the pandemic. UR - https://www.jmir.org/2024/1/e63476 UR - http://dx.doi.org/10.2196/63476 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63476 ER - TY - JOUR AU - Georgescu, Livia Alexandra AU - Cummins, Nicholas AU - Molimpakis, Emilia AU - Giacomazzi, Eduardo AU - Rodrigues Marczyk, Joana AU - Goria, Stefano PY - 2024/12/12 TI - Screening for Depression and Anxiety Using a Nonverbal Working Memory Task in a Sample of Older Brazilians: Observational Study of Preliminary Artificial Intelligence Model Transferability JO - JMIR Form Res SP - e55856 VL - 8 KW - depression KW - anxiety KW - Brazil KW - machine learning KW - n-back KW - working memory KW - artificial intelligence KW - gerontology KW - older adults KW - mental health KW - AI KW - transferability KW - detection KW - screening KW - questionnaire KW - longitudinal study N2 - Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)?driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations. Objective: This study aims to illustrate the preliminary transferability of two established AI models designed to detect high depression and anxiety symptom scores. The models were initially trained on data from a nonverbal working memory game (1- and 2-back tasks) in a dataset by thymia, a company that develops AI solutions for mental health and well-being assessments, encompassing over 6000 participants from the United Kingdom, United States, Mexico, Spain, and Indonesia. We seek to validate the models? performance by applying it to a new dataset comprising older Brazilian adults, thereby exploring its transferability and generalizability across different demographics and cultures. Methods: A total of 69 Brazilian participants aged 51-92 years old were recruited with the help of Laços Saúde, a company specializing in nurse-led, holistic home care. Participants received a link to the thymia dashboard every Monday and Thursday for 6 months. The dashboard had a set of activities assigned to them that would take 10-15 minutes to complete, which included a 5-minute game with two levels of the n-back tasks. Two Random Forest models trained on thymia data to classify depression and anxiety based on thresholds defined by scores of the Patient Health Questionnaire (8 items) (PHQ-8) ?10 and those of the Generalized Anxiety Disorder Assessment (7 items) (GAD-7) ?10, respectively, were subsequently tested on the Laços Saúde patient cohort. Results: The depression classification model exhibited robust performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.78, a specificity of 0.69, and a sensitivity of 0.72. The anxiety classification model showed an initial AUC of 0.63, with a specificity of 0.58 and a sensitivity of 0.64. This performance surpassed a benchmark model using only age and gender, which had AUCs of 0.47 for PHQ-8 and 0.53 for GAD-7. After recomputing the AUC scores on a cross-sectional subset of the data (the first n-back game session), we found AUCs of 0.79 for PHQ-8 and 0.76 for GAD-7. Conclusions: This study successfully demonstrates the preliminary transferability of two AI models trained on a nonverbal working memory task, one for depression and the other for anxiety classification, to a novel sample of older Brazilian adults. Future research could seek to replicate these findings in larger samples and other cultural contexts. Trial Registration: ISRCTN Registry ISRCTN90727704; https://www.isrctn.com/ISRCTN90727704 UR - https://formative.jmir.org/2024/1/e55856 UR - http://dx.doi.org/10.2196/55856 ID - info:doi/10.2196/55856 ER - TY - JOUR AU - Campbell, Marie Amy AU - Hauton, Chris AU - van Aerle, Ronny AU - Martinez-Urtaza, Jaime PY - 2024/11/28 TI - Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach JO - JMIR Bioinform Biotech SP - e62747 VL - 5 KW - pathogen expansion KW - climate change KW - machine learning KW - ecology KW - evolution KW - vibrio parahaemolyticus KW - sequencing KW - sequence type 3 KW - VpST3 KW - genomics N2 - Background: Environmentally sensitive pathogens exhibit ecological and evolutionary responses to climate change that result in the emergence and global expansion of well-adapted variants. It is imperative to understand the mechanisms that facilitate pathogen emergence and expansion, as well as the drivers behind the mechanisms, to understand and prepare for future pandemic expansions. Objective: The unique, rapid, global expansion of a clonal complex of Vibrio parahaemolyticus (a marine bacterium causing gastroenteritis infections) named Vibrio parahaemolyticus sequence type 3 (VpST3) provides an opportunity to explore the eco-evolutionary drivers of pathogen expansion. Methods: The global expansion of VpST3 was reconstructed using VpST3 genomes, which were then classified into metrics characterizing the stages of this expansion process, indicative of the stages of emergence and establishment. We used machine learning, specifically a random forest classifier, to test a range of ecological and evolutionary drivers for their potential in predicting VpST3 expansion dynamics. Results: We identified a range of evolutionary features, including mutations in the core genome and accessory gene presence, associated with expansion dynamics. A range of random forest classifier approaches were tested to predict expansion classification metrics for each genome. The highest predictive accuracies (ranging from 0.722 to 0.967) were achieved for models using a combined eco-evolutionary approach. While population structure and the difference between introduced and established isolates could be predicted to a high accuracy, our model reported multiple false positives when predicting the success of an introduced isolate, suggesting potential limiting factors not represented in our eco-evolutionary features. Regional models produced for 2 countries reporting the most VpST3 genomes had varying success, reflecting the impacts of class imbalance. Conclusions: These novel insights into evolutionary features and ecological conditions related to the stages of VpST3 expansion showcase the potential of machine learning models using genomic data and will contribute to the future understanding of the eco-evolutionary pathways of climate-sensitive pathogens. UR - https://bioinform.jmir.org/2024/1/e62747 UR - http://dx.doi.org/10.2196/62747 UR - http://www.ncbi.nlm.nih.gov/pubmed/39607996 ID - info:doi/10.2196/62747 ER - TY - JOUR AU - Hirosawa, Takanobu AU - Harada, Yukinori AU - Tokumasu, Kazuki AU - Shiraishi, Tatsuya AU - Suzuki, Tomoharu AU - Shimizu, Taro PY - 2024/11/19 TI - Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports JO - JMIR Form Res SP - e64844 VL - 8 KW - artificial intelligence KW - clinical decision support system KW - generative artificial intelligence KW - large language models KW - natural language processing KW - NLP KW - AI KW - clinical decision making KW - decision support KW - decision making KW - LLM: diagnostic KW - case report KW - diagnosis KW - generative AI KW - LLaMA N2 - Background: Generative artificial intelligence (AI), particularly in the form of large language models, has rapidly developed. The LLaMA series are popular and recently updated from LLaMA2 to LLaMA3. However, the impacts of the update on diagnostic performance have not been well documented. Objective: We conducted a comparative evaluation of the diagnostic performance in differential diagnosis lists generated by LLaMA3 and LLaMA2 for case reports. Methods: We analyzed case reports published in the American Journal of Case Reports from 2022 to 2023. After excluding nondiagnostic and pediatric cases, we input the remaining cases into LLaMA3 and LLaMA2 using the same prompt and the same adjustable parameters. Diagnostic performance was defined by whether the differential diagnosis lists included the final diagnosis. Multiple physicians independently evaluated whether the final diagnosis was included in the top 10 differentials generated by LLaMA3 and LLaMA2. Results: In our comparative evaluation of the diagnostic performance between LLaMA3 and LLaMA2, we analyzed differential diagnosis lists for 392 case reports. The final diagnosis was included in the top 10 differentials generated by LLaMA3 in 79.6% (312/392) of the cases, compared to 49.7% (195/392) for LLaMA2, indicating a statistically significant improvement (P<.001). Additionally, LLaMA3 showed higher performance in including the final diagnosis in the top 5 differentials, observed in 63% (247/392) of cases, compared to LLaMA2?s 38% (149/392, P<.001). Furthermore, the top diagnosis was accurately identified by LLaMA3 in 33.9% (133/392) of cases, significantly higher than the 22.7% (89/392) achieved by LLaMA2 (P<.001). The analysis across various medical specialties revealed variations in diagnostic performance with LLaMA3 consistently outperforming LLaMA2. Conclusions: The results reveal that the LLaMA3 model significantly outperforms LLaMA2 per diagnostic performance, with a higher percentage of case reports having the final diagnosis listed within the top 10, top 5, and as the top diagnosis. Overall diagnostic performance improved almost 1.5 times from LLaMA2 to LLaMA3. These findings support the rapid development and continuous refinement of generative AI systems to enhance diagnostic processes in medicine. However, these findings should be carefully interpreted for clinical application, as generative AI, including the LLaMA series, has not been approved for medical applications such as AI-enhanced diagnostics. UR - https://formative.jmir.org/2024/1/e64844 UR - http://dx.doi.org/10.2196/64844 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64844 ER - TY - JOUR AU - Li, Shiyuan AU - Yu, Xiao AU - Ma, Xinrong AU - Wang, Ying AU - Guo, Junjie AU - Wang, Jiping AU - Shen, Wenxin AU - Dong, Hongyu AU - Salvi, Richard AU - Wang, Hui AU - Yin, Shankai PY - 2024/11/14 TI - Optimizing a Classification Model to Evaluate Individual Susceptibility in Noise-Induced Hearing Loss: Cross-Sectional Study JO - JMIR Public Health Surveill SP - e60373 VL - 10 KW - noise-induced hearing loss KW - susceptible KW - resistance KW - machine learning algorithms KW - linear regression KW - extended high frequencies KW - phenotypic characteristics KW - genetic heterogeneity N2 - Background: Noise-induced hearing loss (NIHL), one of the leading causes of hearing loss in young adults, is a major health care problem that has negative social and economic consequences. It is commonly recognized that individual susceptibility largely varies among individuals who are exposed to similar noise. An objective method is, therefore, needed to identify those who are extremely sensitive to noise-exposed jobs to prevent them from developing severe NIHL. Objective: This study aims to determine an optimal model for detecting individuals susceptible or resistant to NIHL and further explore phenotypic traits uniquely associated with their susceptibility profiles. Methods: Cross-sectional data on hearing loss caused by occupational noise were collected from 2015 to 2021 at shipyards in Shanghai, China. Six methods were summarized from the literature review and applied to evaluate their classification performance for susceptibility and resistance of participants to NIHL. A machine learning (ML)?based diagnostic model using frequencies from 0.25 to 12 kHz was developed to determine the most reliable frequencies, considering accuracy and area under the curve. An optimal method with the most reliable frequencies was then constructed to detect individuals who were susceptible versus resistant to NIHL. Phenotypic characteristics such as age, exposure time, cumulative noise exposure, and hearing thresholds (HTs) were explored to identify these groups. Results: A total of 6276 participants (median age 41, IQR 33?47 years; n=5372, 85.6% men) were included in the analysis. The ML-based NIHL diagnostic model with misclassified subjects showed the best performance for identifying workers in the NIHL-susceptible group (NIHL-SG) and NIHL-resistant group (NIHL-RG). The mean HTs at 4 and 12.5 kHz showed the highest predictive value for detecting those in the NIHL-SG and NIHL-RG (accuracy=0.78 and area under the curve=0.81). Individuals in the NIHL-SG selected by the optimized model were younger than those in the NIHL-RG (median 28, IQR 25?31 years vs median 35, IQR 32?39 years; P<.001), with a shorter duration of noise exposure (median 5, IQR 2?8 years vs median 8, IQR 4?12 years; P<.001) and lower cumulative noise exposure (median 90, IQR 86?92 dBA-years vs median 92.2, IQR 89.2?94.7 dBA-years; P<.001) but greater HTs (4 and 12.5 kHz; median 58.8, IQR 53.8?63.8 dB HL vs median 8.8, IQR 7.5?11.3 dB HL; P<.001). Conclusions: An ML-based NIHL diagnostic model with misclassified subjects using the mean HTs of 4 and 12.5 kHz was the most reliable method for identifying individuals susceptible or resistant to NIHL. However, further studies are needed to determine the genetic factors that govern NIHL susceptibility. Trial Registration: Chinese Clinical Trial Registry ChiCTR-RPC-17012580; https://www.chictr.org.cn/showprojEN.html?proj=21399 UR - https://publichealth.jmir.org/2024/1/e60373 UR - http://dx.doi.org/10.2196/60373 ID - info:doi/10.2196/60373 ER - TY - JOUR AU - Chung, Jane AU - Pretzer-Aboff, Ingrid AU - Parsons, Pamela AU - Falls, Katherine AU - Bulut, Eyuphan PY - 2024/11/12 TI - Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study JO - JMIR Res Protoc SP - e53447 VL - 13 KW - Wi-Fi sensing KW - dementia KW - mild cognitive impairment KW - older adults KW - health disparities KW - in-home activities KW - mobility KW - machine learning N2 - Background: Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. Objective: This study aims to examine the feasibility of developing a novel channel state information?based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. Methods: This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. Results: The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing?based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults? Wi-Fi sensing technology acceptance. Conclusions: This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. International Registered Report Identifier (IRRID): DERR1-10.2196/53447 UR - https://www.researchprotocols.org/2024/1/e53447 UR - http://dx.doi.org/10.2196/53447 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53447 ER - TY - JOUR AU - Chung, young Wou AU - Yoon, Jinsik AU - Yoon, Dukyong AU - Kim, Songsoo AU - Kim, Yujeong AU - Park, Eun Ji AU - Kang, Ae Young PY - 2024/11/7 TI - Development and Validation of Deep Learning?Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study JO - J Med Internet Res SP - e58413 VL - 26 KW - pulmonary tuberculosis KW - chest radiography KW - artificial intelligence KW - tuberculosis KW - TB KW - smear KW - smear test KW - culture test KW - diagnosis KW - treatment KW - deep learning KW - CXR KW - PTB KW - management KW - cost effective KW - asymptomatic infection KW - diagnostic tools KW - infectivity KW - AI tool KW - cohort N2 - Background: Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditional diagnostic tests such as smear and culture tests, which can require hours to weeks to yield results. Objective: This study aimed to use artificial intelligence (AI)?based chest radiography (CXR) to evaluate the infectivity of patients with PTB more quickly and accurately compared with traditional methods such as smear and culture tests. Methods: We used DenseNet121 and visualization techniques such as gradient-weighted class activation mapping and local interpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXR images of 4492 patients with PTB obtained from Severance Hospital, focusing specifically on the lung region through segmentation and cropping with TransUNet. We used data from 2004 to 2020 to train the model, data from 2021 for testing, and data from 2022 to 2023 for internal validation. In addition, we used 1978 CXR images of 299 patients with PTB obtained from Yongin Severance Hospital for external validation. Results: In the internal validation, the model achieved an accuracy of 73.27%, an area under the receiver operating characteristic curve of 0.79, and an area under the precision-recall curve of 0.77. In the external validation, it exhibited an accuracy of 70.29%, an area under the receiver operating characteristic curve of 0.77, and an area under the precision-recall curve of 0.8. In addition, gradient-weighted class activation mapping and local interpretable model-agnostic explanations provided insights into the decision-making process of the AI model. Conclusions: This proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, with significant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings, compared with traditional smear and culture tests. UR - https://www.jmir.org/2024/1/e58413 UR - http://dx.doi.org/10.2196/58413 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58413 ER - TY - JOUR AU - Paiva, Bruno AU - Gonçalves, André Marcos AU - da Rocha, Dutra Leonardo Chaves AU - Marcolino, Soriano Milena AU - Lana, Barbosa Fernanda Cristina AU - Souza-Silva, Rego Maira Viana AU - Almeida, M. Jussara AU - Pereira, Delfino Polianna AU - de Andrade, Valiense Claudio Moisés AU - Gomes, Reis Angélica Gomides dos AU - Ferreira, Pires Maria Angélica AU - Bartolazzi, Frederico AU - Sacioto, Furtado Manuela AU - Boscato, Paula Ana AU - Guimarães-Júnior, Henriques Milton AU - dos Reis, Pereira Priscilla AU - Costa, Roberto Felício AU - Jorge, Oliveira Alzira de AU - Coelho, Reis Laryssa AU - Carneiro, Marcelo AU - Sales, Souza Thaís Lorenna AU - Araújo, Ferreira Silvia AU - Silveira, Vitório Daniel AU - Ruschel, Brasil Karen AU - Santos, Veloso Fernanda Caldeira AU - Cenci, Almeida Evelin Paola de AU - Menezes, Monteiro Luanna Silva AU - Anschau, Fernando AU - Bicalho, Camargos Maria Aparecida AU - Manenti, Fernandes Euler Roberto AU - Finger, Goulart Renan AU - Ponce, Daniela AU - de Aguiar, Carrilho Filipe AU - Marques, Margoto Luiza AU - de Castro, César Luís AU - Vietta, Grünewald Giovanna AU - Godoy, de Mariana Frizzo AU - Vilaça, Nascimento Mariana do AU - Morais, Costa Vivian PY - 2024/10/28 TI - A New Natural Language Processing?Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study JO - JMIR Med Inform SP - e54246 VL - 12 KW - health care KW - machine learning KW - data drifts KW - temporal drifts N2 - Background: Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. Objective: This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzing changes in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. Methods: The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing?inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Results: Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. Conclusions: We developed a novel methodology combining machine learning and natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimately improving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper. UR - https://medinform.jmir.org/2024/1/e54246 UR - http://dx.doi.org/10.2196/54246 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/54246 ER - TY - JOUR AU - Wagner, K. Jennifer AU - Doerr, Megan AU - Schmit, D. Cason PY - 2024/9/30 TI - AI Governance: A Challenge for Public Health JO - JMIR Public Health Surveill SP - e58358 VL - 10 KW - artificial intelligence KW - legislation and jurisprudence KW - harm reduction KW - social determinants of health KW - one health KW - AI KW - invisible algorithms KW - modern life KW - public health KW - engagement KW - AI governance KW - traditional regulation KW - soft law UR - https://publichealth.jmir.org/2024/1/e58358 UR - http://dx.doi.org/10.2196/58358 ID - info:doi/10.2196/58358 ER - TY - JOUR AU - Dong, Xing-Xuan AU - Huang, Yueqing AU - Miao, Yi-Fan AU - Hu, Hui-Hui AU - Pan, Chen-Wei AU - Zhang, Tianyang AU - Wu, Yibo PY - 2024/9/12 TI - Personality and Health-Related Quality of Life of Older Chinese Adults: Cross-Sectional Study and Moderated Mediation Model Analysis JO - JMIR Public Health Surveill SP - e57437 VL - 10 KW - personality KW - health-related quality of life KW - older adults KW - sleep quality KW - quality of life KW - old KW - older KW - Chinese KW - China KW - mechanisms KW - psychology KW - behavior KW - analysis KW - hypothesis KW - neuroticism KW - mediation analysis KW - health care providers KW - aging N2 - Background: Personality has an impact on the health-related quality of life (HRQoL) of older adults. However, the relationship and mechanisms of the 2 variables are controversial, and few studies have been conducted on older adults. Objective: The aim of this study was to explore the relationship between personality and HRQoL and the mediating and moderating roles of sleep quality and place of residence in this relationship. Methods: A total of 4123 adults 60 years and older were from the Psychology and Behavior Investigation of Chinese Residents survey. Participants were asked to complete the Big Five Inventory, the Brief version of the Pittsburgh Sleep Quality Index, and EQ-5D-5L. A backpropagation neural network was used to explore the order of factors contributing to HRQoL. Path analysis was performed to evaluate the mediation hypothesis. Results: As of August 31, 2022, we enrolled 4123 older adults 60 years and older. Neuroticism and extraversion were strong influencing factors of HRQoL (normalized importance >50%). The results of the mediation analysis suggested that neuroticism and extraversion may enhance and diminish, respectively, HRQoL (index: ?=?.262, P<.001; visual analog scale: ?=?.193, P<.001) by increasing and decreasing brief version of the Pittsburgh Sleep Quality Index scores (neuroticism: ?=.17, P<.001; extraversion: ?=?.069, P<.001). The multigroup analysis suggested a significant moderating effect of the place of residence (EQ-5D-5L index: P<.001; EQ-5D-5L visual analog scale: P<.001). No significant direct effect was observed between extraversion and EQ-5D-5L index in urban older residents (?=.037, P=.73). Conclusions: This study sheds light on the potential mechanisms of personality and HRQoL among older Chinese adults and can help health care providers and relevant departments take reasonable measures to promote healthy aging. UR - https://publichealth.jmir.org/2024/1/e57437 UR - http://dx.doi.org/10.2196/57437 ID - info:doi/10.2196/57437 ER - TY - JOUR AU - Oyebola, Kolapo AU - Ligali, Funmilayo AU - Owoloye, Afolabi AU - Erinwusi, Blessing AU - Alo, Yetunde AU - Musa, Z. Adesola AU - Aina, Oluwagbemiga AU - Salako, Babatunde PY - 2024/9/11 TI - Machine Learning?Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals JO - JMIRx Med SP - e56993 VL - 5 KW - hyperglycemia KW - diabetes KW - machine learning KW - hypertension KW - random forest N2 - Background: Noncommunicable diseases continue to pose a substantial health challenge globally, with hyperglycemia serving as a prominent indicator of diabetes. Objective: This study employed machine learning algorithms to predict hyperglycemia in a cohort of individuals who were asymptomatic and unraveled crucial predictors contributing to early risk identification. Methods: This dataset included an extensive array of clinical and demographic data obtained from 195 adults who were asymptomatic and residing in a suburban community in Nigeria. The study conducted a thorough comparison of multiple machine learning algorithms to ascertain the most effective model for predicting hyperglycemia. Moreover, we explored feature importance to pinpoint correlates of high blood glucose levels within the cohort. Results: Elevated blood pressure and prehypertension were recorded in 8 (4.1%) and 18 (9.2%) of the 195 participants, respectively. A total of 41 (21%) participants presented with hypertension, of which 34 (83%) were female. However, sex adjustment showed that 34 of 118 (28.8%) female participants and 7 of 77 (9%) male participants had hypertension. Age-based analysis revealed an inverse relationship between normotension and age (r=?0.88; P=.02). Conversely, hypertension increased with age (r=0.53; P=.27), peaking between 50?59 years. Of the 195 participants, isolated systolic hypertension and isolated diastolic hypertension were recorded in 16 (8.2%) and 15 (7.7%) participants, respectively, with female participants recording a higher prevalence of isolated systolic hypertension (11/16, 69%) and male participants reporting a higher prevalence of isolated diastolic hypertension (11/15, 73%). Following class rebalancing, the random forest classifier gave the best performance (accuracy score 0.89; receiver operating characteristic?area under the curve score 0.89; F1-score 0.89) of the 26 model classifiers. The feature selection model identified uric acid and age as important variables associated with hyperglycemia. Conclusions: The random forest classifier identified significant clinical correlates associated with hyperglycemia, offering valuable insights for the early detection of diabetes and informing the design and deployment of therapeutic interventions. However, to achieve a more comprehensive understanding of each feature?s contribution to blood glucose levels, modeling additional relevant clinical features in larger datasets could be beneficial. UR - https://xmed.jmir.org/2024/1/e56993 UR - http://dx.doi.org/10.2196/56993 ID - info:doi/10.2196/56993 ER - TY - JOUR AU - Zaghir, Jamil AU - Naguib, Marco AU - Bjelogrlic, Mina AU - Névéol, Aurélie AU - Tannier, Xavier AU - Lovis, Christian PY - 2024/9/10 TI - Prompt Engineering Paradigms for Medical Applications: Scoping Review JO - J Med Internet Res SP - e60501 VL - 26 KW - prompt engineering KW - prompt design KW - prompt learning KW - prompt tuning KW - large language models KW - LLMs KW - scoping review KW - clinical natural language processing KW - natural language processing KW - NLP KW - medical texts KW - medical application KW - medical applications KW - clinical practice KW - privacy KW - medicine KW - computer science KW - medical informatics N2 - Background: Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. Objective: The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. Methods: Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering?based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). Results: We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering?specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. Conclusions: In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field. UR - https://www.jmir.org/2024/1/e60501 UR - http://dx.doi.org/10.2196/60501 UR - http://www.ncbi.nlm.nih.gov/pubmed/39255030 ID - info:doi/10.2196/60501 ER - TY - JOUR AU - Pellemans, Mathijs AU - Salmi, Salim AU - Mérelle, Saskia AU - Janssen, Wilco AU - van der Mei, Rob PY - 2024/8/1 TI - Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial JO - J Med Internet Res SP - e53562 VL - 26 KW - motivational interviewing KW - behavioral coding KW - suicide prevention KW - artificial intelligence KW - effectiveness KW - counseling KW - support tool KW - online help KW - mental health N2 - Background: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. Objective: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. Methods: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. Results: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen ?=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, ?=0.65) and evocative and nonevocative language (AUC=0.92, ?=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, ?=0.55). The model?s interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. Conclusions: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback. UR - https://www.jmir.org/2024/1/e53562 UR - http://dx.doi.org/10.2196/53562 UR - http://www.ncbi.nlm.nih.gov/pubmed/39088244 ID - info:doi/10.2196/53562 ER - TY - JOUR AU - Laymouna, Moustafa AU - Ma, Yuanchao AU - Lessard, David AU - Schuster, Tibor AU - Engler, Kim AU - Lebouché, Bertrand PY - 2024/7/23 TI - Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review JO - J Med Internet Res SP - e56930 VL - 26 KW - chatbot KW - conversational agent KW - conversational assistant KW - user-computer interface KW - digital health KW - mobile health KW - electronic health KW - telehealth KW - artificial intelligence KW - AI KW - health information technology N2 - Background: Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots? roles, users, benefits, and limitations is available to inform future research and application in the field. Objective: This review aims to describe health care chatbots? characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations. Methods: A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis. Results: The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts. Conclusions: Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use. UR - https://www.jmir.org/2024/1/e56930 UR - http://dx.doi.org/10.2196/56930 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56930 ER - TY - JOUR AU - Chen, Xi AU - Wang, Li AU - You, MingKe AU - Liu, WeiZhi AU - Fu, Yu AU - Xu, Jie AU - Zhang, Shaoting AU - Chen, Gang AU - Li, Kang AU - Li, Jian PY - 2024/7/22 TI - Evaluating and Enhancing Large Language Models? Performance in Domain-Specific Medicine: Development and Usability Study With DocOA JO - J Med Internet Res SP - e58158 VL - 26 KW - large language model KW - retrieval-augmented generation KW - domain-specific benchmark framework KW - osteoarthritis management N2 - Background: The efficacy of large language models (LLMs) in domain-specific medicine, particularly for managing complex diseases such as osteoarthritis (OA), remains largely unexplored. Objective: This study focused on evaluating and enhancing the clinical capabilities and explainability of LLMs in specific domains, using OA management as a case study. Methods: A domain-specific benchmark framework was developed to evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM designed for OA management integrating retrieval-augmented generation and instructional prompts, was developed. It can identify the clinical evidence upon which its answers are based through retrieval-augmented generation, thereby demonstrating the explainability of those answers. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations. Results: Results showed that general LLMs such as GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements. Conclusions: This study introduces a novel benchmark framework that assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs. UR - https://www.jmir.org/2024/1/e58158 UR - http://dx.doi.org/10.2196/58158 UR - http://www.ncbi.nlm.nih.gov/pubmed/38833165 ID - info:doi/10.2196/58158 ER - TY - JOUR AU - Leung, W. Yvonne AU - Wouterloot, Elise AU - Adikari, Achini AU - Hong, Jinny AU - Asokan, Veenaajaa AU - Duan, Lauren AU - Lam, Claire AU - Kim, Carlina AU - Chan, P. Kai AU - De Silva, Daswin AU - Trachtenberg, Lianne AU - Rennie, Heather AU - Wong, Jiahui AU - Esplen, Jane Mary PY - 2024/7/22 TI - Artificial Intelligence?Based Co-Facilitator (AICF) for Detecting and Monitoring Group Cohesion Outcomes in Web-Based Cancer Support Groups: Single-Arm Trial Study JO - JMIR Cancer SP - e43070 VL - 10 KW - group cohesion KW - LIWC KW - online support group KW - natural language processing KW - NLP KW - emotion analysis KW - machine learning KW - sentiment analysis KW - emotion detection KW - integrating human knowledge KW - emotion lining KW - cancer KW - oncology KW - support group KW - artificial intelligence KW - AI KW - therapy KW - online therapist KW - emotion KW - affect KW - speech tagging KW - speech tag KW - topic modeling KW - named entity recognition KW - spoken language processing KW - focus group KW - corpus KW - language KW - linguistic N2 - Background: Commonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of nonverbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence?based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics. Objective: The aim of this study was to develop a method to train and evaluate AICF?s capacity to monitor group cohesion. Methods: AICF used a text classification approach to extract the mentions of group cohesion within conversations. A sample of data was annotated by human scorers, which was used as the training data to build the classification model. The annotations were further supported by finding contextually similar group cohesion expressions using word embedding models as well. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC). Results: AICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine learning algorithms combined with human input could detect group cohesion, a clinically meaningful indicator of effective OSGs. After retraining with human input, AICF reached an F1-score of 0.82. AICF performed slightly better at identifying group cohesion compared to LIWC. Conclusions: AICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in web-based settings by attending to individual needs. International Registered Report Identifier (IRRID): RR2-10.2196/21453 UR - https://cancer.jmir.org/2024/1/e43070 UR - http://dx.doi.org/10.2196/43070 UR - http://www.ncbi.nlm.nih.gov/pubmed/39037754 ID - info:doi/10.2196/43070 ER - TY - JOUR AU - Liu, Pinxin AU - Lou, Xubin AU - Xie, Zidian AU - Shang, Ce AU - Li, Dongmei PY - 2024/7/11 TI - Public Perceptions and Discussions of the US Food and Drug Administration's JUUL Ban Policy on Twitter: Observational Study JO - JMIR Form Res SP - e51327 VL - 8 KW - e-cigarettes KW - JUUL KW - Twitter KW - deep learning KW - FDA KW - Food and Drug Administration KW - vape KW - vaping KW - smoking KW - social media KW - regulation N2 - Background: On June 23, 2022, the US Food and Drug Administration announced a JUUL ban policy, to ban all vaping and electronic cigarette products sold by Juul Labs. Objective: This study aims to understand public perceptions and discussions of this policy using Twitter (subsequently rebranded as X) data. Methods: Using the Twitter streaming application programming interface, 17,007 tweets potentially related to the JUUL ban policy were collected between June 22, 2022, and July 25, 2022. Based on 2600 hand-coded tweets, a deep learning model (RoBERTa) was trained to classify all tweets into propolicy, antipolicy, neutral, and irrelevant categories. A deep learning model (M3 model) was used to estimate basic demographics (such as age and gender) of Twitter users. Furthermore, major topics were identified using latent Dirichlet allocation modeling. A logistic regression model was used to examine the association of different Twitter users with their attitudes toward the policy. Results: Among 10,480 tweets related to the JUUL ban policy, there were similar proportions of propolicy and antipolicy tweets (n=2777, 26.5% vs n=2666, 25.44%). Major propolicy topics included ?JUUL causes youth addition,? ?market surge of JUUL,? and ?health effects of JUUL.? In contrast, major antipolicy topics included ?cigarette should be banned instead of JUUL,? ?against the irrational policy,? and ?emotional catharsis.? Twitter users older than 29 years were more likely to be propolicy (have a positive attitude toward the JUUL ban policy) than those younger than 29 years. Conclusions: Our study showed that the public showed different responses to the JUUL ban policy, which varies depending on the demographic characteristics of Twitter users. Our findings could provide valuable information to the Food and Drug Administration for future electronic cigarette and other tobacco product regulations. UR - https://formative.jmir.org/2024/1/e51327 UR - http://dx.doi.org/10.2196/51327 UR - http://www.ncbi.nlm.nih.gov/pubmed/38990633 ID - info:doi/10.2196/51327 ER - TY - JOUR AU - Liu, Pei AU - Liu, Yijun AU - Liu, Hao AU - Xiong, Linping AU - Mei, Changlin AU - Yuan, Lei PY - 2024/6/3 TI - A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study JO - Asian Pac Isl Nurs J SP - e48378 VL - 8 KW - chronic kidney disease KW - random forest model KW - risk factors KW - assessment N2 - Background: The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression. Objective: In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD. Methods: We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing?an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model. Results: The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm?based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%. Conclusions: Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD. UR - https://apinj.jmir.org/2024/1/e48378 UR - http://dx.doi.org/10.2196/48378 UR - http://www.ncbi.nlm.nih.gov/pubmed/38830204 ID - info:doi/10.2196/48378 ER - TY - JOUR AU - Mohebbi, Fahimeh AU - Forati, Masoud Amir AU - Torres, Lucas AU - deRoon-Cassini, A. Terri AU - Harris, Jennifer AU - Tomas, W. Carissa AU - Mantsch, R. John AU - Ghose, Rina PY - 2024/5/3 TI - Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis JO - JMIR Public Health Surveill SP - e52691 VL - 10 KW - machine learning KW - geospatial KW - racial disparities KW - social determinant of health KW - structural racism KW - mental health KW - health disparities KW - deep learning N2 - Background: Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation. Objective: This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies. Methods: We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health. Results: While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors?smoking, poverty, insufficient sleep, lack of health insurance, employment, and age?were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health. Conclusions: The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism. UR - https://publichealth.jmir.org/2024/1/e52691 UR - http://dx.doi.org/10.2196/52691 UR - http://www.ncbi.nlm.nih.gov/pubmed/38701436 ID - info:doi/10.2196/52691 ER - TY - JOUR AU - Huguet, Nathalie AU - Chen, Jinying AU - Parikh, B. Ravi AU - Marino, Miguel AU - Flocke, A. Susan AU - Likumahuwa-Ackman, Sonja AU - Bekelman, Justin AU - DeVoe, E. Jennifer PY - 2024/4/22 TI - Applying Machine Learning Techniques to Implementation Science JO - Online J Public Health Inform SP - e50201 VL - 16 KW - implementation science KW - machine learning KW - implementation strategies KW - techniques KW - implementation KW - prediction KW - adaptation KW - acceptance KW - challenges KW - scientist UR - https://ojphi.jmir.org/2024/1/e50201 UR - http://dx.doi.org/10.2196/50201 UR - http://www.ncbi.nlm.nih.gov/pubmed/38648094 ID - info:doi/10.2196/50201 ER - TY - JOUR AU - Jonathan, Joan AU - Barakabitze, Alex Alcardo AU - Fast, D. Cynthia AU - Cox, Christophe PY - 2024/4/16 TI - Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats JO - Online J Public Health Inform SP - e50771 VL - 16 KW - machine learning KW - African giant pouched rat KW - diagnosis KW - tuberculosis KW - health care N2 - Background: Technological advancement has led to the growth and rapid increase of tuberculosis (TB) medical data generated from different health care areas, including diagnosis. Prioritizing better adoption and acceptance of innovative diagnostic technology to reduce the spread of TB significantly benefits developing countries. Trained TB-detection rats are used in Tanzania and Ethiopia for operational research to complement other TB diagnostic tools. This technology has increased new TB case detection owing to its speed, cost-effectiveness, and sensitivity. Objective: During the TB detection process, rats produce vast amounts of data, providing an opportunity to identify interesting patterns that influence TB detection performance. This study aimed to develop models that predict if the rat will hit (indicate the presence of TB within) the sample or not using machine learning (ML) techniques. The goal was to improve the diagnostic accuracy and performance of TB detection involving rats. Methods: APOPO (Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling) Center in Morogoro provided data for this study from 2012 to 2019, and 366,441 observations were used to build predictive models using ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor, by incorporating a variety of variables, such as the diagnostic results from partner health clinics using methods endorsed by the World Health Organization (WHO). Results: The support vector machine technique yielded the highest accuracy of 83.39% for prediction compared to other ML techniques used. Furthermore, this study found that the inclusion of variables related to whether the sample contained TB or not increased the performance accuracy of the predictive model. Conclusions: The inclusion of variables related to the diagnostic results of TB samples may improve the detection performance of the trained rats. The study results may be of importance to TB-detection rat trainers and TB decision-makers as the results may prompt them to take action to maintain the usefulness of the technology and increase the TB detection performance of trained rats. UR - https://ojphi.jmir.org/2024/1/e50771 UR - http://dx.doi.org/10.2196/50771 UR - http://www.ncbi.nlm.nih.gov/pubmed/38625737 ID - info:doi/10.2196/50771 ER - TY - JOUR AU - Hernández Guillamet, Guillem AU - Morancho Pallaruelo, Ning Ariadna AU - Miró Mezquita, Laura AU - Miralles, Ramón AU - Mas, Àngel Miquel AU - Ulldemolins Papaseit, José María AU - Estrada Cuxart, Oriol AU - López Seguí, Francesc PY - 2023/12/28 TI - Machine Learning Model for Predicting Mortality Risk in Patients With Complex Chronic Conditions: Retrospective Analysis JO - Online J Public Health Inform SP - e52782 VL - 15 KW - machine learning KW - mortality prediction KW - chronicity KW - chromic KW - complex KW - artificial intelligence KW - complexity KW - health data KW - predict KW - prediction KW - predictive KW - mortality KW - death KW - classification KW - algorithm KW - algorithms KW - mortality risk KW - risk prediction N2 - Background: The health care system is undergoing a shift toward a more patient-centered approach for individuals with chronic and complex conditions, which presents a series of challenges, such as predicting hospital needs and optimizing resources. At the same time, the exponential increase in health data availability has made it possible to apply advanced statistics and artificial intelligence techniques to develop decision-support systems and improve resource planning, diagnosis, and patient screening. These methods are key to automating the analysis of large volumes of medical data and reducing professional workloads. Objective: This article aims to present a machine learning model and a case study in a cohort of patients with highly complex conditions. The object was to predict mortality within the following 4 years and early mortality over 6 months following diagnosis. The method used easily accessible variables and health care resource utilization information. Methods: A classification algorithm was selected among 6 models implemented and evaluated using a stratified cross-validation strategy with k=10 and a 70/30 train-test split. The evaluation metrics used included accuracy, recall, precision, F1-score, and area under the receiver operating characteristic (AUROC) curve. Results: The model predicted patient death with an 87% accuracy, recall of 87%, precision of 82%, F1-score of 84%, and area under the curve (AUC) of 0.88 using the best model, the Extreme Gradient Boosting (XGBoost) classifier. The results were worse when predicting premature deaths (following 6 months) with an 83% accuracy (recall=55%, precision=64% F1-score=57%, and AUC=0.88) using the Gradient Boosting (GRBoost) classifier. Conclusions: This study showcases encouraging outcomes in forecasting mortality among patients with intricate and persistent health conditions. The employed variables are conveniently accessible, and the incorporation of health care resource utilization information of the patient, which has not been employed by current state-of-the-art approaches, displays promising predictive power. The proposed prediction model is designed to efficiently identify cases that need customized care and proactively anticipate the demand for critical resources by health care providers. UR - https://ojphi.jmir.org/2023/1/e52782 UR - http://dx.doi.org/10.2196/52782 UR - http://www.ncbi.nlm.nih.gov/pubmed/38223690 ID - info:doi/10.2196/52782 ER - TY - JOUR AU - Bragazzi, Luigi Nicola AU - Crapanzano, Andrea AU - Converti, Manlio AU - Zerbetto, Riccardo AU - Khamisy-Farah, Rola PY - 2023/12/6 TI - The Impact of Generative Conversational Artificial Intelligence on the Lesbian, Gay, Bisexual, Transgender, and Queer Community: Scoping Review JO - J Med Internet Res SP - e52091 VL - 25 KW - generative conversational artificial intelligence KW - chatbot KW - lesbian, gay, bisexual, transgender, and queer community KW - LGBTQ KW - scoping review KW - mobile phone N2 - Background: Despite recent significant strides toward acceptance, inclusion, and equality, members of the lesbian, gay, bisexual, transgender, and queer (LGBTQ) community still face alarming mental health disparities, being almost 3 times more likely to experience depression, anxiety, and suicidal thoughts than their heterosexual counterparts. These unique psychological challenges are due to discrimination, stigmatization, and identity-related struggles and can potentially benefit from generative conversational artificial intelligence (AI). As the latest advancement in AI, conversational agents and chatbots can imitate human conversation and support mental health, fostering diversity and inclusivity, combating stigma, and countering discrimination. In contrast, if not properly designed, they can perpetuate exclusion and inequities. Objective: This study aims to examine the impact of generative conversational AI on the LGBTQ community. Methods: This study was designed as a scoping review. Four electronic scholarly databases (Scopus, Embase, Web of Science, and MEDLINE via PubMed) and gray literature (Google Scholar) were consulted from inception without any language restrictions. Original studies focusing on the LGBTQ community or counselors working with this community exposed to chatbots and AI-enhanced internet-based platforms and exploring the feasibility, acceptance, or effectiveness of AI-enhanced tools were deemed eligible. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Results: Seven applications (HIVST-Chatbot, TelePrEP Navigator, Amanda Selfie, Crisis Contact Simulator, REALbot, Tough Talks, and Queer AI) were included and reviewed. The chatbots and internet-based assistants identified served various purposes: (1) to identify LGBTQ individuals at risk of suicide or contracting HIV or other sexually transmitted infections, (2) to provide resources to LGBTQ youth from underserved areas, (3) facilitate HIV status disclosure to sex partners, and (4) develop training role-play personas encompassing the diverse experiences and intersecting identities of LGBTQ youth to educate counselors. The use of generative conversational AI for the LGBTQ community is still in its early stages. Initial studies have found that deploying chatbots is feasible and well received, with high ratings for usability and user satisfaction. However, there is room for improvement in terms of the content provided and making conversations more engaging and interactive. Many of these studies used small sample sizes and short-term interventions measuring limited outcomes. Conclusions: Generative conversational AI holds promise, but further development and formal evaluation are needed, including studies with larger samples, longer interventions, and randomized trials to compare different content, delivery methods, and dissemination platforms. In addition, a focus on engagement with behavioral objectives is essential to advance this field. The findings have broad practical implications, highlighting that AI?s impact spans various aspects of people?s lives. Assessing AI?s impact on diverse communities and adopting diversity-aware and intersectional approaches can help shape AI?s positive impact on society as a whole. UR - https://www.jmir.org/2023/1/e52091 UR - http://dx.doi.org/10.2196/52091 UR - http://www.ncbi.nlm.nih.gov/pubmed/37864350 ID - info:doi/10.2196/52091 ER - TY - JOUR AU - Bougeard, Stéphanie AU - Huneau-Salaun, Adeline AU - Attia, Mikael AU - Richard, Jean-Baptiste AU - Demeret, Caroline AU - Platon, Johnny AU - Allain, Virginie AU - Le Vu, Stéphane AU - Goyard, Sophie AU - Gillon, Véronique AU - Bernard-Stoecklin, Sibylle AU - Crescenzo-Chaigne, Bernadette AU - Jones, Gabrielle AU - Rose, Nicolas AU - van der Werf, Sylvie AU - Lantz, Olivier AU - Rose, Thierry AU - Noël, Harold PY - 2023/11/28 TI - Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study JO - JMIR Public Health Surveill SP - e46898 VL - 9 KW - SARS-CoV-2 KW - serological surveillance KW - infection KW - vaccination KW - machine learning KW - seroprevalence KW - blood testing KW - immunity KW - survey KW - vaccine response KW - French population KW - prediction N2 - Background: The seroprevalence of SARS-CoV-2 infection in the French population was estimated with a representative, repeated cross-sectional survey based on residual sera from routine blood testing. These data contained no information on infection or vaccination status, thus limiting the ability to detail changes observed in the immunity level of the population over time. Objective: Our aim is to predict the infected or vaccinated status of individuals in the French serosurveillance survey based only on the results of serological assays. Reference data on longitudinal serological profiles of seronegative, infected, and vaccinated individuals from another French cohort were used to build the predictive model. Methods: A model of individual vaccination or infection status with respect to SARS-CoV-2 obtained from a machine learning procedure was proposed based on 3 complementary serological assays. This model was applied to the French nationwide serosurveillance survey from March 2020 to March 2022 to estimate the proportions of the population that were negative, infected, vaccinated, or infected and vaccinated. Results: From February 2021 to March 2022, the estimated percentage of infected and unvaccinated individuals in France increased from 7.5% to 16.8%. During this period, the estimated percentage increased from 3.6% to 45.2% for vaccinated and uninfected individuals and from 2.1% to 29.1% for vaccinated and infected individuals. The decrease in the seronegative population can be largely attributed to vaccination. Conclusions: Combining results from the serosurveillance survey with more complete data from another longitudinal cohort completes the information retrieved from serosurveillance while keeping its protocol simple and easy to implement. UR - https://publichealth.jmir.org/2023/1/e46898 UR - http://dx.doi.org/10.2196/46898 UR - http://www.ncbi.nlm.nih.gov/pubmed/38015594 ID - info:doi/10.2196/46898 ER - TY - JOUR AU - Tan, Yun Denise Jia AU - Ko, Ki Tsz AU - Fan, Siu Ka PY - 2023/11/27 TI - The Readability and Quality of Web-Based Patient Information on Nasopharyngeal Carcinoma: Quantitative Content Analysis JO - JMIR Form Res SP - e47762 VL - 7 KW - nasopharyngeal cancer KW - internet information KW - readability KW - Journal of the American Medical Association KW - JAMA KW - DISCERN KW - artificial intelligence KW - AI N2 - Background: Nasopharyngeal carcinoma (NPC) is a rare disease that is strongly associated with exposure to the Epstein-Barr virus and is characterized by the formation of malignant cells in nasopharynx tissues. Early diagnosis of NPC is often difficult owing to the location of initial tumor sites and the nonspecificity of initial symptoms, resulting in a higher frequency of advanced-stage diagnoses and a poorer prognosis. Access to high-quality, readable information could improve the early detection of the disease and provide support to patients during disease management. Objective: This study aims to assess the quality and readability of publicly available web-based information in the English language about NPC, using the most popular search engines. Methods: Key terms relevant to NPC were searched across 3 of the most popular internet search engines: Google, Yahoo, and Bing. The top 25 results from each search engine were included in the analysis. Websites that contained text written in languages other than English, required paywall access, targeted medical professionals, or included nontext content were excluded. Readability for each website was assessed using the Flesch Reading Ease score and the Flesch-Kincaid grade level. Website quality was assessed using the Journal of the American Medical Association (JAMA) and DISCERN tools as well as the presence of a Health on the Net Foundation seal. Results: Overall, 57 suitable websites were included in this study; 26% (15/57) of the websites were academic. The mean JAMA and DISCERN scores of all websites were 2.80 (IQR 3) and 57.60 (IQR 19), respectively, with a median of 3 (IQR 2-4) and 61 (IQR 49-68), respectively. Health care industry websites (n=3) had the highest mean JAMA score of 4 (SD 0). Academic websites (15/57, 26%) had the highest mean DISCERN score of 77.5. The Health on the Net Foundation seal was present on only 1 website, which also achieved a JAMA score of 3 and a DISCERN score of 50. Significant differences were observed between the JAMA score of hospital websites and the scores of industry websites (P=.04), news service websites (P<.048), charity and nongovernmental organization websites (P=.03). Despite being a vital source for patients, general practitioner websites were found to have significantly lower JAMA scores compared with charity websites (P=.05). The overall mean readability scores reflected an average reading age of 14.3 (SD 1.1) years. Conclusions: The results of this study suggest an inconsistent and suboptimal quality of information related to NPC on the internet. On average, websites presented readability challenges, as written information about NPC was above the recommended reading level of sixth grade. As such, web-based information requires improvement in both quality and accessibility, and healthcare providers should be selective about information recommended to patients, ensuring they are reliable and readable. UR - https://formative.jmir.org/2023/1/e47762 UR - http://dx.doi.org/10.2196/47762 UR - http://www.ncbi.nlm.nih.gov/pubmed/38010802 ID - info:doi/10.2196/47762 ER - TY - JOUR AU - Jing, Fengshi AU - Ye, Yang AU - Zhou, Yi AU - Ni, Yuxin AU - Yan, Xumeng AU - Lu, Ying AU - Ong, Jason AU - Tucker, D. Joseph AU - Wu, Dan AU - Xiong, Yuan AU - Xu, Chen AU - He, Xi AU - Huang, Shanzi AU - Li, Xiaofeng AU - Jiang, Hongbo AU - Wang, Cheng AU - Dai, Wencan AU - Huang, Liqun AU - Mei, Wenhua AU - Cheng, Weibin AU - Zhang, Qingpeng AU - Tang, Weiming PY - 2023/11/23 TI - Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach JO - J Med Internet Res SP - e37719 VL - 25 KW - HIV self-testing KW - machine learning KW - MSM KW - men who have sex with men KW - secondary distribution KW - key influencers identification N2 - Background: HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as ?indexes?) applying for multiple kits and subsequently sharing them with people (defined as ?alters?) in their social networks. However, identifying key influencers is difficult. Objective: This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits. Methods: We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach. Results: We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI ?14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention?s efficiency by 17.7% (95% CI ?3.5% to 38.8%) compared to that of human identification. Conclusions: We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits. Trial Registration: Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001 UR - https://www.jmir.org/2023/1/e37719 UR - http://dx.doi.org/10.2196/37719 UR - http://www.ncbi.nlm.nih.gov/pubmed/37995110 ID - info:doi/10.2196/37719 ER - TY - JOUR AU - Dou, Xuelin AU - Liu, Yang AU - Liao, Aijun AU - Zhong, Yuping AU - Fu, Rong AU - Liu, Lihong AU - Cui, Canchan AU - Wang, Xiaohong AU - Lu, Jin PY - 2023/11/2 TI - Patient Journey Toward a Diagnosis of Light Chain Amyloidosis in a National Sample: Cross-Sectional Web-Based Study JO - JMIR Form Res SP - e44420 VL - 7 KW - systemic light chain amyloidosis KW - AL amyloidosis KW - rare disease KW - big data KW - network analysis KW - machine model KW - natural language processing KW - web-based N2 - Background: Systemic light chain (AL) amyloidosis is a rare and multisystem disease associated with increased morbidity and a poor prognosis. Delayed diagnoses are common due to the heterogeneity of the symptoms. However, real-world insights from Chinese patients with AL amyloidosis have not been investigated. Objective: This study aimed to describe the journey to an AL amyloidosis diagnosis and to build an in-depth understanding of the diagnostic process from the perspective of both clinicians and patients to obtain a correct and timely diagnosis. Methods: Publicly available disease-related content from social media platforms between January 2008 and April 2021 was searched. After performing data collection steps with a machine model, a series of disease-related posts were extracted. Natural language processing was used to identify the relevance of variables, followed by further manual evaluation and analysis. Results: A total of 2204 valid posts related to AL amyloidosis were included in this study, of which 1968 were posted on haodf.com. Of these posts, 1284 were posted by men (median age 57, IQR 46-67 years); 1459 posts mentioned renal-related symptoms, followed by heart (n=833), liver (n=491), and stomach (n=368) symptoms. Furthermore, 1502 posts mentioned symptoms related to 2 or more organs. Symptoms for AL amyloidosis most frequently mentioned by suspected patients were nonspecific weakness (n=252), edema (n=196), hypertrophy (n=168), and swelling (n=140). Multiple physician visits were common, and nephrologists (n=265) and hematologists (n=214) were the most frequently visited specialists by suspected patients for initial consultation. Additionally, interhospital referrals were also commonly seen, centralizing in tertiary hospitals. Conclusions: Chinese patients with AL amyloidosis experienced referrals during their journey toward accurate diagnosis. Increasing awareness of the disease and early referral to a specialized center with expertise may reduce delayed diagnosis and improve patient management. UR - https://formative.jmir.org/2023/1/e44420 UR - http://dx.doi.org/10.2196/44420 UR - http://www.ncbi.nlm.nih.gov/pubmed/37917132 ID - info:doi/10.2196/44420 ER - TY - JOUR AU - Yang, Liuyang AU - Zhang, Ting AU - Han, Xuan AU - Yang, Jiao AU - Sun, Yanxia AU - Ma, Libing AU - Chen, Jialong AU - Li, Yanming AU - Lai, Shengjie AU - Li, Wei AU - Feng, Luzhao AU - Yang, Weizhong PY - 2023/10/17 TI - Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study JO - J Med Internet Res SP - e45085 VL - 25 KW - early warning KW - epidemic intelligence KW - infectious disease KW - influenza-like illness KW - surveillance N2 - Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models. UR - https://www.jmir.org/2023/1/e45085 UR - http://dx.doi.org/10.2196/45085 UR - http://www.ncbi.nlm.nih.gov/pubmed/37847532 ID - info:doi/10.2196/45085 ER - TY - JOUR AU - Zhou, Weipeng AU - Prater, C. Laura AU - Goldstein, V. Evan AU - Mooney, J. Stephen PY - 2023/10/17 TI - Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A Large Language Model Approach JO - JMIR Ment Health SP - e49359 VL - 10 KW - female firearm suicide KW - large language model KW - document classification KW - suicide prevention KW - suicide KW - firearm suicide KW - machine learning KW - mental health for women KW - violent death KW - mental health KW - language models KW - women KW - female KW - depression KW - suicidal N2 - Background: Firearm suicide has been more prevalent among males, but age-adjusted female firearm suicide rates increased by 20% from 2010 to 2020, outpacing the rate increase among males by about 8 percentage points, and female firearm suicide may have different contributing circumstances. In the United States, the National Violent Death Reporting System (NVDRS) is a comprehensive source of data on violent deaths and includes unstructured incident narrative reports from coroners or medical examiners and law enforcement. Conventional natural language processing approaches have been used to identify common circumstances preceding female firearm suicide deaths but failed to identify rarer circumstances due to insufficient training data. Objective: This study aimed to leverage a large language model approach to identify infrequent circumstances preceding female firearm suicide in the unstructured coroners or medical examiners and law enforcement narrative reports available in the NVDRS. Methods: We used the narrative reports of 1462 female firearm suicide decedents in the NVDRS from 2014 to 2018. The reports were written in English. We coded 9 infrequent circumstances preceding female firearm suicides. We experimented with predicting those circumstances by leveraging a large language model approach in a yes/no question-answer format. We measured the prediction accuracy with F1-score (ranging from 0 to 1). F1-score is the harmonic mean of precision (positive predictive value) and recall (true positive rate or sensitivity). Results: Our large language model outperformed a conventional support vector machine?supervised machine learning approach by a wide margin. Compared to the support vector machine model, which had F1-scores less than 0.2 for most infrequent circumstances, our large language model approach achieved an F1-score of over 0.6 for 4 circumstances and 0.8 for 2 circumstances. Conclusions: The use of a large language model approach shows promise. Researchers interested in using natural language processing to identify infrequent circumstances in narrative report data may benefit from large language models. UR - https://mental.jmir.org/2023/1/e49359 UR - http://dx.doi.org/10.2196/49359 UR - http://www.ncbi.nlm.nih.gov/pubmed/37847549 ID - info:doi/10.2196/49359 ER - TY - JOUR AU - Passanante, Aly AU - Pertwee, Ed AU - Lin, Leesa AU - Lee, Yoonsup Kristi AU - Wu, T. Joseph AU - Larson, J. Heidi PY - 2023/10/3 TI - Conversational AI and Vaccine Communication: Systematic Review of the Evidence JO - J Med Internet Res SP - e42758 VL - 25 KW - chatbots KW - artificial intelligence KW - conversational AI KW - vaccine communication KW - vaccine hesitancy KW - conversational agent KW - COVID-19 KW - vaccine information KW - health information N2 - Background: Since the mid-2010s, use of conversational artificial intelligence (AI; chatbots) in health care has expanded significantly, especially in the context of increased burdens on health systems and restrictions on in-person consultations with health care providers during the COVID-19 pandemic. One emerging use for conversational AI is to capture evolving questions and communicate information about vaccines and vaccination. Objective: The objective of this systematic review was to examine documented uses and evidence on the effectiveness of conversational AI for vaccine communication. Methods: This systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Web of Science, PsycINFO, MEDLINE, Scopus, CINAHL Complete, Cochrane Library, Embase, Epistemonikos, Global Health, Global Index Medicus, Academic Search Complete, and the University of London library database were searched for papers on the use of conversational AI for vaccine communication. The inclusion criteria were studies that included (1) documented instances of conversational AI being used for the purpose of vaccine communication and (2) evaluation data on the impact and effectiveness of the intervention. Results: After duplicates were removed, the review identified 496 unique records, which were then screened by title and abstract, of which 38 were identified for full-text review. Seven fit the inclusion criteria and were assessed and summarized in the findings of this review. Overall, vaccine chatbots deployed to date have been relatively simple in their design and have mainly been used to provide factual information to users in response to their questions about vaccines. Additionally, chatbots have been used for vaccination scheduling, appointment reminders, debunking misinformation, and, in some cases, for vaccine counseling and persuasion. Available evidence suggests that chatbots can have a positive effect on vaccine attitudes; however, studies were typically exploratory in nature, and some lacked a control group or had very small sample sizes. Conclusions: The review found evidence of potential benefits from conversational AI for vaccine communication. Factors that may contribute to the effectiveness of vaccine chatbots include their ability to provide credible and personalized information in real time, the familiarity and accessibility of the chatbot platform, and the extent to which interactions with the chatbot feel ?natural? to users. However, evaluations have focused on the short-term, direct effects of chatbots on their users. The potential longer-term and societal impacts of conversational AI have yet to be analyzed. In addition, existing studies do not adequately address how ethics apply in the field of conversational AI around vaccines. In a context where further digitalization of vaccine communication can be anticipated, additional high-quality research will be required across all these areas. UR - https://www.jmir.org/2023/1/e42758 UR - http://dx.doi.org/10.2196/42758 UR - http://www.ncbi.nlm.nih.gov/pubmed/37788057 ID - info:doi/10.2196/42758 ER - TY - JOUR AU - Parab, Shubham AU - Boster, Jerry AU - Washington, Peter PY - 2023/9/29 TI - Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study JO - JMIR Form Res SP - e49898 VL - 7 KW - Parkinson disease KW - digital health KW - machine learning KW - remote screening KW - accessible screening N2 - Background: Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD. Objective: Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease. Methods: Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement. Results: Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD. Conclusions: This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD. UR - https://formative.jmir.org/2023/1/e49898 UR - http://dx.doi.org/10.2196/49898 UR - http://www.ncbi.nlm.nih.gov/pubmed/37773607 ID - info:doi/10.2196/49898 ER - TY - JOUR AU - Li, Ziyu AU - Wu, Xiaoqian AU - Xu, Lin AU - Liu, Ming AU - Huang, Cheng PY - 2023/9/21 TI - Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling JO - J Med Internet Res SP - e45019 VL - 25 KW - topic model KW - health rumors KW - social media KW - WeChat official account KW - content analysis KW - public health KW - machine learning KW - Twitter KW - social network KW - misinformation KW - users KW - disease KW - diet N2 - Background: Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public?s physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. Objective: This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. Methods: We used a web crawler tool to capture health rumor?dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. Results: We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. Conclusions: Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis?related studies be conducted to verify the impact of health rumor?related topics. UR - https://www.jmir.org/2023/1/e45019 UR - http://dx.doi.org/10.2196/45019 UR - http://www.ncbi.nlm.nih.gov/pubmed/37733396 ID - info:doi/10.2196/45019 ER - TY - JOUR AU - Loebenberg, Gemma AU - Oldham, Melissa AU - Brown, Jamie AU - Dinu, Larisa AU - Michie, Susan AU - Field, Matt AU - Greaves, Felix AU - Garnett, Claire PY - 2023/9/14 TI - Bot or Not? Detecting and Managing Participant Deception When Conducting Digital Research Remotely: Case Study of a Randomized Controlled Trial JO - J Med Internet Res SP - e46523 VL - 25 KW - artificial intelligence KW - false information KW - mHealth applications KW - participant deception KW - participant KW - recruit KW - research subject KW - web-based studies N2 - Background: Evaluating digital interventions using remote methods enables the recruitment of large numbers of participants relatively conveniently and cheaply compared with in-person methods. However, conducting research remotely based on participant self-report with little verification is open to automated ?bots? and participant deception. Objective: This paper uses a case study of a remotely conducted trial of an alcohol reduction app to highlight and discuss (1) the issues with participant deception affecting remote research trials with financial compensation; and (2) the importance of rigorous data management to detect and address these issues. Methods: We recruited participants on the internet from July 2020 to March 2022 for a randomized controlled trial (n=5602) evaluating the effectiveness of an alcohol reduction app, Drink Less. Follow-up occurred at 3 time points, with financial compensation offered (up to £36 [US $39.23]). Address authentication and telephone verification were used to detect 2 kinds of deception: ?bots,? that is, automated responses generated in clusters; and manual participant deception, that is, participants providing false information. Results: Of the 1142 participants who enrolled in the first 2 months of recruitment, 75.6% (n=863) of them were identified as bots during data screening. As a result, a CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) was added, and after this, no more bots were identified. Manual participant deception occurred throughout the study. Of the 5956 participants (excluding bots) who enrolled in the study, 298 (5%) were identified as false participants. The extent of this decreased from 110 in November 2020, to a negligible level by February 2022 including a number of months with 0. The decline occurred after we added further screening questions such as attention checks, removed the prominence of financial compensation from social media advertising, and added an additional requirement to provide a mobile phone number for identity verification. Conclusions: Data management protocols are necessary to detect automated bots and manual participant deception in remotely conducted trials. Bots and manual deception can be minimized by adding a CAPTCHA, attention checks, a requirement to provide a phone number for identity verification, and not prominently advertising financial compensation on social media. Trial Registration: ISRCTN Number ISRCTN64052601; https://doi.org/10.1186/ISRCTN64052601 UR - https://www.jmir.org/2023/1/e46523 UR - http://dx.doi.org/10.2196/46523 UR - http://www.ncbi.nlm.nih.gov/pubmed/37707943 ID - info:doi/10.2196/46523 ER - TY - JOUR AU - Lee, Ji-Soo AU - Lee, Soo-Kyoung PY - 2023/9/12 TI - Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study JO - JMIR Form Res SP - e42756 VL - 7 KW - latent class analysis KW - machine learning KW - metabolic syndrome KW - risk factor KW - single-person households N2 - Background: The rapid increase of single-person households in South Korea is leading to an increase in the incidence of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases, due to lifestyle changes. It is necessary to analyze the complex effects of metabolic syndrome risk factors in South Korean single-person households, which differ from one household to another, considering the diversity of single-person households. Objective: This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning techniques and categorically characterize the risk factors through latent class analysis (LCA). Methods: This cross-sectional study included 10-year secondary data obtained from the National Health and Nutrition Examination Survey (2009-2018). We selected 1371 participants belonging to single-person households. Data were analyzed using SPSS (version 25.0; IBM Corp), Mplus (version 8.0; Muthen & Muthen), and Python (version 3.0; Plone & Python). We applied 4 machine learning algorithms (logistic regression, decision tree, random forest, and extreme gradient boost) to identify important factors and then applied LCA to categorize the risk groups of metabolic syndromes in single-person households. Results: Through LCA, participants were classified into 4 groups (group 1: intense physical activity in early adulthood, group 2: hypertension among middle-aged female respondents, group 3: smoking and drinking among middle-aged male respondents, and group 4: obesity and abdominal obesity among middle-aged respondents). In addition, age, BMI, obesity, subjective body shape recognition, alcohol consumption, smoking, binge drinking frequency, and job type were investigated as common factors that affect metabolic syndrome in single-person households through machine learning techniques. Group 4 was the most susceptible and at-risk group for metabolic syndrome (odds ratio 17.67, 95% CI 14.5-25.3; P<.001), and obesity and abdominal obesity were the most influential risk factors for metabolic syndrome. Conclusions: This study identified risk groups and factors affecting metabolic syndrome in single-person households through machine learning techniques and LCA. Through these findings, customized interventions for each generational risk factor for metabolic syndrome can be implemented, leading to the prevention of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases. In conclusion, this study contributes to the prevention of metabolic syndrome in single-person households by providing new insights and priority groups for the development of customized interventions using classification. UR - https://formative.jmir.org/2023/1/e42756 UR - http://dx.doi.org/10.2196/42756 UR - http://www.ncbi.nlm.nih.gov/pubmed/37698907 ID - info:doi/10.2196/42756 ER - TY - JOUR AU - Sallam, Malik AU - Salim, A. Nesreen AU - Barakat, Muna AU - Al-Mahzoum, Kholoud AU - Al-Tammemi, B. Ala'a AU - Malaeb, Diana AU - Hallit, Rabih AU - Hallit, Souheil PY - 2023/9/5 TI - Assessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study JO - JMIR Med Educ SP - e48254 VL - 9 KW - artificial intelligence KW - machine learning KW - education KW - technology KW - healthcare KW - survey KW - opinion KW - knowledge KW - practices KW - KAP N2 - Background: ChatGPT is a conversational large language model that has the potential to revolutionize knowledge acquisition. However, the impact of this technology on the quality of education is still unknown considering the risks and concerns surrounding ChatGPT use. Therefore, it is necessary to assess the usability and acceptability of this promising tool. As an innovative technology, the intention to use ChatGPT can be studied in the context of the technology acceptance model (TAM). Objective: This study aimed to develop and validate a TAM-based survey instrument called TAME-ChatGPT (Technology Acceptance Model Edited to Assess ChatGPT Adoption) that could be employed to examine the successful integration and use of ChatGPT in health care education. Methods: The survey tool was created based on the TAM framework. It comprised 13 items for participants who heard of ChatGPT but did not use it and 23 items for participants who used ChatGPT. Using a convenient sampling approach, the survey link was circulated electronically among university students between February and March 2023. Exploratory factor analysis (EFA) was used to assess the construct validity of the survey instrument. Results: The final sample comprised 458 respondents, the majority among them undergraduate students (n=442, 96.5%). Only 109 (23.8%) respondents had heard of ChatGPT prior to participation and only 55 (11.3%) self-reported ChatGPT use before the study. EFA analysis on the attitude and usage scales showed significant Bartlett tests of sphericity scores (P<.001) and adequate Kaiser-Meyer-Olkin measures (0.823 for the attitude scale and 0.702 for the usage scale), confirming the factorability of the correlation matrices. The EFA showed that 3 constructs explained a cumulative total of 69.3% variance in the attitude scale, and these subscales represented perceived risks, attitude to technology/social influence, and anxiety. For the ChatGPT usage scale, EFA showed that 4 constructs explained a cumulative total of 72% variance in the data and comprised the perceived usefulness, perceived risks, perceived ease of use, and behavior/cognitive factors. All the ChatGPT attitude and usage subscales showed good reliability with Cronbach ? values >.78 for all the deduced subscales. Conclusions: The TAME-ChatGPT demonstrated good reliability, validity, and usefulness in assessing health care students? attitudes toward ChatGPT. The findings highlighted the importance of considering risk perceptions, usefulness, ease of use, attitudes toward technology, and behavioral factors when adopting ChatGPT as a tool in health care education. This information can aid the stakeholders in creating strategies to support the optimal and ethical use of ChatGPT and to identify the potential challenges hindering its successful implementation. Future research is recommended to guide the effective adoption of ChatGPT in health care education. UR - https://mededu.jmir.org/2023/1/e48254 UR - http://dx.doi.org/10.2196/48254 UR - http://www.ncbi.nlm.nih.gov/pubmed/37578934 ID - info:doi/10.2196/48254 ER - TY - JOUR AU - Gniadek, Thomas AU - Kang, Jason AU - Theparee, Talent AU - Krive, Jacob PY - 2023/9/1 TI - Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine JO - Online J Public Health Inform SP - e50934 VL - 15 KW - explainable artificial intelligence KW - XAI KW - artificial intelligence KW - AI KW - AI medicine KW - pathology informatics KW - radiology informatics UR - https://ojphi.jmir.org/2023/1/e50934 UR - http://dx.doi.org/10.2196/50934 UR - http://www.ncbi.nlm.nih.gov/pubmed/38046562 ID - info:doi/10.2196/50934 ER - TY - JOUR PY - 2022// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e12851 VL - 14 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v14i1.12851 UR - http://www.ncbi.nlm.nih.gov/pubmed/36685053 ID - info:doi/10.5210/ojphi.v14i1.12851 ER - TY - JOUR PY - 2017// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e7605 VL - 9 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v9i1.7605 ID - info:doi/10.5210/ojphi.v9i1.7605 ER - TY - JOUR PY - 2017// TI - Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review JO - Online J Public Health Inform SP - e7650 VL - 9 IS - 1 UR - UR - http://dx.doi.org/10.5210/ojphi.v9i1.7650 ID - info:doi/10.5210/ojphi.v9i1.7650 ER -