Online Journal of Public Health Informatics

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Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Reference 18: Accuracy of GFR estimating equations based on creatinine, cystatin C or both in routine

Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe

JMIR Aging 2025;8:e64473

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Maternal Metabolic Health and Mother and Baby Health Outcomes (MAMBO): Protocol of a Prospective Observational Study

Summary of study activities. a Hb A1c: hemoglobin A1c. b CRP: C-reactive protein. c OGTT: oral glucose tolerance test. At the first visit, a full medical history will be taken by a trained clinician including past medical history and surgical history. Outcomes of previous pregnancies will be recorded including outcome of the pregnancy, gestation, pregnancy complications, and (if relevant) birthweight and neonatal complications. Current medications including dose, and dosing schedule will be recorded.

Sarah A L Price, Digsu N Koye, Alice Lewin, Alison Nankervis, Stefan C Kane

JMIR Res Protoc 2025;14:e72542

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Decentralized Biobanking Apps for Patient Tracking of Biospecimen Research: Real-World Usability and Feasibility Study

We examined all contexts along the data pipeline, from population-level breast cancer screening to diagnostic biopsies and surgical treatments, clinical pathology, and specimen accessioning through the biobanking platform, where it may be stored for future use in –80 °C freezers or distributed fresh for next-generation biobanking applications such as patient-derived organoids, multi-omics, and high-throughput testing.

William Sanchez, Ananya Dewan, Eve Budd, M Eifler, Robert C Miller, Jeffery Kahn, Mario Macis, Marielle Gross

JMIR Bioinform Biotech 2025;6:e70463

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Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study

(C) and (D) Performance of training on BIDMC data and testing on MGH data. AUPRC: area under the precision-recall curve; AUROC: area under the receiver operating characteristic curve; BIDMC: Beth Israel Deaconess Medical Center; MGH: Mass General Hospital; PR: precision-recall; ROC: receiver operating characteristic. Logistic regression coefficients from using the model trained with notes, ICDa codes, and medications. Unexpected results are discussed in the Error Analysis section.

Daniel Sumsion, Elijah Davis, Marta Fernandes, Ruoqi Wei, Rebecca Milde, Jet Malou Veltink, Wan-Yee Kong, Yiwen Xiong, Samvrit Rao, Tara Westover, Lydia Petersen, Niels Turley, Arjun Singh, Stephanie Buss, Shibani Mukerji, Sahar Zafar, Sudeshna Das, Valdery Moura Junior, Manohar Ghanta, Aditya Gupta, Jennifer Kim, Katie Stone, Emmanuel Mignot, Dennis Hwang, Lynn Marie Trotti, Gari D Clifford, Umakanth Katwa, Robert Thomas, M Brandon Westover, Haoqi Sun

JMIR Med Inform 2025;13:e64113

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Development of a Mobile Intervention for Procrastination Augmented With a Semigenerative Chatbot for University Students: Pilot Randomized Controlled Trial

Screenshots of the time management app we used for the treatment group: (A) main screen with to-do list; (B) calendar screen visualizing success rate; (C) chatting room screen for conversation with the chatbot Moa. The content that was originally in Korean has been translated into English. Moa is a semigenerative chatbot interlocked with the to-do app to facilitate conversations tailored according to the users’ delaying behavior.

Seonmi Lee, Jaehyun Jeong, Myungsung Kim, Sangil Lee, Sung-Phil Kim, Dooyoung Jung

JMIR Mhealth Uhealth 2025;13:e53133

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A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

Age Sex Systolic BPa Diastolic BP BMI Chronic kidney disease Diabetes Hypertension Cerebrovascular disease Coronary artery disease COPDb Liver cirrhosis Smoking Preoperative ACEic or ARBd usage Preoperative NSAIDe usage Department Weekday Operation duration White blood cell count Hemoglobin C-reactive protein Glucose Urea nitrogen Creatinine e GFRf Total protein Albumin ASTg ALTh Sodium Potassium Chloride Calcium Uric acid Creatine phosphokinase Lactic dehydrogenase Urine specific gravity Urine protein a BP: blood

Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon

J Med Internet Res 2025;27:e62853

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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

We developed a prediction model by integrating data from preoperative laboratory measurements 16 routinely measured basic parameters: white blood cell, hemoglobin, platelet count, aspartate aminotransferase, alanine aminotransferase, blood urea nitrogen, creatinine, albumin, calcium, sodium, phosphate, total bilirubin, c-reactive protein, cholesterol, hemoglobin A1c, and prothrombin time), previous diagnosis, medication records, and surgical type from the SNUBH CDM development dataset.

Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh