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

Artificial Intelligence (922) Heart Failure Self-Management (129) Clinical Information and Decision Making (1125) Machine Learning (1194) Decision Support for Health Professionals (930) Intensive Care Unit (ICU) (90) Clinical Informatics (706) Secondary Use of Clinical Data for Research and Surveillance (268)

Published on in Vol 24, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38082, first published .
Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study

Authors of this article:

Jili Li 1 Author Orcid Image ;   Siru Liu 2 Author Orcid Image ;   Yundi Hu 3 Author Orcid Image ;   Lingfeng Zhu 4 Author Orcid Image ;   Yujia Mao 1 Author Orcid Image ;   Jialin Liu 5 Author Orcid Image
Article Authors Cited by (12) Tweetations (6) Metrics

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  11. Jia T, Xu K, Bai Y, Lv M, Shan L, Li W, Zhang X, Li Z, Wang Z, Zhao X, Li M, Zhang Y. Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study. BMC Medical Informatics and Decision Making 2023;23(1) View