This paper is in the following e-collection/theme issue:

Presentations, Abstracts, or Demonstrations (740) Machine Learning (1370) Influenza and Influenza-Like-Illness (ILI) Surveillance (203)

Published on in Vol 5, No 1 (2013):

Detection of Patients with Influenza Syndrome Using Machine-Learning Models Learned from Emergency Department Reports

Detection of Patients with Influenza Syndrome Using Machine-Learning Models Learned from Emergency Department Reports

Detection of Patients with Influenza Syndrome Using Machine-Learning Models Learned from Emergency Department Reports

Authors of this article:

Arturo Lopez Pineda1 ;   Fu-Chiang Tsui1 ;   Shyam Visweswaran1 ;   Gregory F. Cooper1
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    Information available in ED reports has the potential to improve detection of syndromic diseases. Our goal is to provide a machine-learning model characterized by improved predictive accuracy of influenza syndrome. Seven machine-learning algorithms (K2-BN, NB, EBMC, SVM, LR, ANN, RF) for the construction of models were used. Our dataset correspond to 40853 ED cases (67% training, 33% testing). The measurements used were AUROC, calibration and statistical significance testing. The results show high AUROCs with no significant difference between the algorithms and the expert model. EBMC is the most general algorithms.