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

Clinical Information and Decision Making (1423) JMIR Theme Issue: COVID-19 Special Issue (2457) Machine Learning (1488) Decision Support for Health Professionals (1201) Infectious Diseases (non-STD/STI) (1584) Tools, Programs and Algorithms (272) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1519) Outbreak and Pandemic Preparedness and Management (1737)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31549, first published .
The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

Authors of this article:

Fang He1, 2 Author Orcid Image ;   John H Page3 Author Orcid Image ;   Kerry R Weinberg2 Author Orcid Image ;   Anirban Mishra2 Author Orcid Image
Article Authors Cited by (17) Tweetations (3) Metrics

Fang He   1, 2 , BChE, PhD ;   John H Page   3 , MD, SCD ;   Kerry R Weinberg   2 , BSc, MBA, MSci ;   Anirban Mishra   2 , BSc, MSc

1 Amgen Inc, Center for Observational Research, South San Francisco, CA, United States

2 Amgen Inc, Digital Health & Innovation, Thousand Oaks, CA, United States

3 Amgen Inc, Center for Observational Research, Thousand Oaks, CA, United States

Corresponding Author:

  • Fang He, BChE, PhD
  • Amgen Inc
  • Center for Observational Research
  • 1120 Veterans Boulevard
  • South San Francisco, CA, 94080
  • United States
  • Phone: 1 925-216-1658
  • Email: fhe01@amgen.com