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

Artificial Intelligence (2129) Cardiology (224) Machine Learning (2076) Cardiac Risk and Cardiac Risk Calculators (102) Clinical Information and Decision Making (1915) Clinical Informatics (1325) Decision Support for Health Professionals (1539) New Methods (1134) Advanced Data Analytics in eHealth (209)

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 (10) Tweetations Metrics

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  1. Graham B, Farrell M. Mortality prediction using data from wearable activity trackers and individual characteristics: An explainable artificial intelligence approach. Expert Systems with Applications 2025;267:126195 View
  2. Bouktif S, Khanday A, Ouni A. Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study. Journal of Medical Internet Research 2025;27:e65434 View
  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
  5. Tang J, Li Y, Luo K, Lai J, Yin X, Wu D. Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study. JMIR Formative Research 2025;9:e67311 View
  6. Yao L, Liu Y, Wang T, Han C, Li Q, Li Q, You X, Ren T, Wang Y. Global trends of big data analytics in health research: a bibliometric study. Frontiers in Medicine 2025;12 View
  7. Xie P, Wang H, Xiao J, Xu F, Guo R, Xie B, Li T, Ma R, Ding H, Ma Y, Xiao J. Generative AI-powered explainable prediction model: Enhancing early in-hospital mortality alert for patients with acute myocardial infarction. International Journal of Cardiology 2025;440:133649 View
  8. Sun H, Kang M, Zhang H, Jia J, Wang Q. Machine Learning for Predicting Mortality in Intensive Care Unit Patients: A Prognostic Performance Systematic Review and Meta‐Analysis. Nursing in Critical Care 2025;30(6) View
  9. Feng H, Zhou F, Shen Y, Wang Z, Yuan Y, Jing W, Zheng Z, Peng H, Yu Q. Prognostic Value of the Charlson Comorbidity Index for Mortality and Machine Learning–Based Prediction in Critically Ill Patients with Paralytic Ileus: Retrospective Cohort Study. JMIR Medical Informatics 2025;13:e76003 View
  10. Xie P, Hu Y, Li J, Ma Y, Xiao J. Unlocking the potential of real-time ICU mortality prediction: redefining risk assessment with continuous data recovery. npj Digital Medicine 2025;8(1) View