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 (2452) Machine Learning (1465) Electronic Health Records (1053) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Imaging Informatics (193) Artificial Intelligence (1371) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

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 (35) Tweetations (1) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.