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

Artificial Intelligence (1371) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Electronic Health Records (1053) Machine Learning (1465) Cardiology (159) Secondary Use of Clinical Data for Research and Surveillance (367) Myocardial Infarction (Heart Attack) (44) Cardiac Risk and Cardiac Risk Calculators (45) New Methods (944)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27798, first published .
Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

Predicting the Mortality and Readmission of In-Hospital Cardiac Arrest Patients With Electronic Health Records: A Machine Learning Approach

Authors of this article:

Chien-Yu Chi1 Author Orcid Image ;   Shuang Ao2 Author Orcid Image ;   Adrian Winkler2 Author Orcid Image ;   Kuan-Chun Fu2 Author Orcid Image ;   Jie Xu2 Author Orcid Image ;   Yi-Lwun Ho3 Author Orcid Image ;   Chien-Hua Huang4 Author Orcid Image ;   Rohollah Soltani2 Author Orcid Image
Article Authors Cited by (6) Tweetations (3) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.