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

Theme Issue: Medical Informatics and COVID-19 (107) JMIR Theme Issue: COVID-19 Special Issue (2461) Machine Learning (1501) Electronic Health Records (1068) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Imaging Informatics (200) Artificial Intelligence (1421) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24572, first published .
Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Authors of this article:

Juan Carlos Quiroz1, 2 Author Orcid Image ;   You-Zhen Feng3 Author Orcid Image ;   Zhong-Yuan Cheng3 Author Orcid Image ;   Dana Rezazadegan1, 4 Author Orcid Image ;   Ping-Kang Chen3 Author Orcid Image ;   Qi-Ting Lin3 Author Orcid Image ;   Long Qian5 Author Orcid Image ;   Xiao-Fang Liu6, 7 Author Orcid Image ;   Shlomo Berkovsky1 Author Orcid Image ;   Enrico Coiera1 Author Orcid Image ;   Lei Song8 Author Orcid Image ;   Xiaoming Qiu9 Author Orcid Image ;   Sidong Liu1 Author Orcid Image ;   Xiang-Ran Cai3 Author Orcid Image
Article Authors Cited by (36) Tweetations (1) Metrics

Journals

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Books/Policy Documents

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