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

Artificial Intelligence (1723) Cardiology (194) Machine Learning (1730) Cardiac Risk and Cardiac Risk Calculators (67) Clinical Information and Decision Making (1647) Clinical Informatics (1160) Decision Support for Health Professionals (1337) New Methods (1030) Advanced Data Analytics in eHealth (167)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49848, first published .
Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study

Authors of this article:

Puguang Xie1 Author Orcid Image ;   Hao Wang1 Author Orcid Image ;   Jun Xiao1 Author Orcid Image ;   Fan Xu1 Author Orcid Image ;   Jingyang Liu1 Author Orcid Image ;   Zihang Chen2 Author Orcid Image ;   Weijie Zhao2 Author Orcid Image ;   Siyu Hou3 Author Orcid Image ;   Dongdong Wu4 Author Orcid Image ;   Yu Ma1 Author Orcid Image ;   Jingjing Xiao3 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

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  3. Xu L, Zhao W, He J, Hou S, He J, Zhuang Y, Wang Y, Yang H, Xiao J, Qiu Y. Abdominal perfusion pressure is critical for survival analysis in patients with intra-abdominal hypertension: mortality prediction using incomplete data. International Journal of Surgery 2025;111(1):371 View
  4. Zhang Y, Liu H, Huang Q, Qu W, Shi Y, Zhang T, Li J, Chen J, Shi Y, Deng R, Chen Y, Zhang Z. Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis. International Journal of Medical Informatics 2025;198:105875 View