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

JMIR Theme Issue: COVID-19 Special Issue (2388) Clinical Information and Decision Making (1314) Decision Support for Health Professionals (1127) Machine Learning (1370) Theme Issue: Medical Informatics and COVID-19 (103) Infectious Diseases (non-STD/STI) (1478) Outbreak and Pandemic Preparedness and Management (1673) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481) Intensive Care Unit (ICU) (101)

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

Hyung-Jun Kim   1 * , MD ;   Deokjae Han   1 * , MD ;   Jeong-Han Kim   2 , MD ;   Daehyun Kim   3 , DDS ;   Beomman Ha   4 , MD, PhD ;   Woong Seog   4 , MD ;   Yeon-Kyeng Lee   5 , PhD ;   Dosang Lim   5 , MSc ;   Sung Ok Hong   5 , PhD ;   Mi-Jin Park   5 , MSc ;   JoonNyung Heo   4 , MD

1 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Armed Forces Capital Hospital, Seongnam, Republic of Korea

2 Division of Infectious Diseases, Department of Internal Medicine, Armed Forces Capital Hospital, Seongnam, Republic of Korea

3 Department of Periodontology, Armed Forces Capital Hospital, Seongnam, Republic of Korea

4 The Armed Forces Medical Command, Seongnam, Republic of Korea

5 Division of Chronic Disease Control, Korea Center for Disease Control and Prevention, Cheongju, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • JoonNyung Heo, MD
  • The Armed Forces Medical Command
  • 81 Saemaeul-ro 177beon-gil, Bundang-gu
  • Seongnam
  • Republic of Korea
  • Phone: 82 31 725 5490
  • Email: jnheo@jnheo.com