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COVID-19 Outcome Prediction and Monitoring Solution for Military Hospitals in South Korea: Development and Evaluation of an Application

JoonNyung Heo, Ji Ae Park, Deokjae Han, Hyung-Jun Kim, Daeun Ahn, Beomman Ha, Woong Seog, Yu Rang Park

J Med Internet Res 2020;22(11):e22131

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Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

In 2016, the CDC launched Flu Sight on the Epidemic Prediction Initiative website to forecast weekly influenza activity [18].

Aye Moa, David Muscatello, Abrar Chughtai, Xin Chen, C Raina MacIntyre

JMIR Public Health Surveill 2019;5(3):e11780

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Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach

Daowei Li, Qiang Zhang, Yue Tan, Xinghuo Feng, Yuanyi Yue, Yuhan Bai, Jimeng Li, Jiahang Li, Youjun Xu, Shiyu Chen, Si-Yu Xiao, Muyan Sun, Xiaona Li, Fang Zhu

JMIR Med Inform 2020;8(11):e21604

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A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

Mingyue Hu, Xinhui Shu, Gang Yu, Xinyin Wu, Maritta Välimäki, Hui Feng

J Med Internet Res 2021;23(2):e20298

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Clinical Predictive Models for COVID-19: Systematic Study

Patrick Schwab, August DuMont Schütte, Benedikt Dietz, Stefan Bauer

J Med Internet Res 2020;22(10):e21439

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Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

To optimize the prediction accuracy, often the methods do not attempt to produce interpretable models.

Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk

J Med Internet Res 2016;18(12):e323

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Prognostic Machine Learning Models for First-Year Mortality in Incident Hemodialysis Patients: Development and Validation Study

Kaixiang Sheng, Ping Zhang, Xi Yao, Jiawei Li, Yongchun He, Jianghua Chen

JMIR Med Inform 2020;8(10):e20578

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Identifying Potential Norovirus Epidemics in China via Internet Surveillance

Prediction models were constructed to explore the relationship of the mean composite Baidu Index and potential case number under diverse optimal lag periods, respectively.

Kui Liu, Sichao Huang, Zi-Ping Miao, Bin Chen, Tao Jiang, Gaofeng Cai, Zhenggang Jiang, Yongdi Chen, Zhengting Wang, Hua Gu, Chengliang Chai, Jianmin Jiang

J Med Internet Res 2017;19(8):e282

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Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

Lei Zhang, Xianwen Shang, Subhashaan Sreedharan, Xixi Yan, Jianbin Liu, Stuart Keel, Jinrong Wu, Wei Peng, Mingguang He

JMIR Med Inform 2020;8(7):e16850

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Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

S(i) denotes the prediction for the ith observation using the simplified model, P(i) denotes the prediction for the ith observation using the ML model, and Pavg denotes the average prediction from the ML model.

Shannon Wongvibulsin, Katherine C Wu, Scott L Zeger

JMIR Med Inform 2020;8(6):e15791

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