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

JMIR Theme Issue: COVID-19 Special Issue (2452) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Machine Learning (1465) Theme Issue: Medical Informatics and COVID-19 (107) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Intensive Care Unit (ICU) (112)

Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24225, first published .
An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

Authors of this article:

Hyung-Jun Kim1 Author Orcid Image ;   Deokjae Han1 Author Orcid Image ;   Jeong-Han Kim2 Author Orcid Image ;   Daehyun Kim3 Author Orcid Image ;   Beomman Ha4 Author Orcid Image ;   Woong Seog4 Author Orcid Image ;   Yeon-Kyeng Lee5 Author Orcid Image ;   Dosang Lim5 Author Orcid Image ;   Sung Ok Hong5 Author Orcid Image ;   Mi-Jin Park5 Author Orcid Image ;   JoonNyung Heo4 Author Orcid Image
Article Authors Cited by (40) Tweetations (12) Metrics

Journals

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  30. Mohammedain S, Badran S, Elzouki A, Salim H, Chalaby A, Siddiqui M, Hussein Y, Rahim H, Thalib L, Alam M, Al-Badriyeh D, Al-Maadeed S, Doi S. Validation of a Risk Prediction Model for COVID-19: The PERIL Prospective Cohort Study. Future Virology 2023;18(15):991 View
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Books/Policy Documents

  1. Sharma R, Pandey H, Agarwal A, Srivastava D. Proceedings of Fourth Doctoral Symposium on Computational Intelligence. View