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

Artificial Intelligence (2144) Machine Learning (2084) Cardiac Risk and Cardiac Risk Calculators (105) Decision Support for Health Professionals (1550)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47664, first published .
Development and Validation of Machine Learning–Based Models to Predict In-Hospital Mortality in Life-Threatening Ventricular Arrhythmias: Retrospective Cohort Study

Development and Validation of Machine Learning–Based Models to Predict In-Hospital Mortality in Life-Threatening Ventricular Arrhythmias: Retrospective Cohort Study

Development and Validation of Machine Learning–Based Models to Predict In-Hospital Mortality in Life-Threatening Ventricular Arrhythmias: Retrospective Cohort Study

Authors of this article:

Le Li1 Author Orcid Image ;   Ligang Ding1 Author Orcid Image ;   Zhuxin Zhang1 Author Orcid Image ;   Likun Zhou1 Author Orcid Image ;   Zhenhao Zhang1 Author Orcid Image ;   Yulong Xiong1 Author Orcid Image ;   Zhao Hu1 Author Orcid Image ;   Yan Yao1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

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  2. Hu Z, Wang M, Zheng S, Xu X, Zhang Z, Ge Q, Li J, Yao Y. Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study. JMIR Human Factors 2024;11:e55802 View
  3. Pan J, Guo T, Kong H, Bu W, Shao M, Geng Z. Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning. Scientific Reports 2025;15(1) View
  4. Liu Y, Wu Q, Zhou L, Tang Y, Li F, Li S. CONSTRUCTING A DIAGNOSTIC PREDICTION MODEL TO ESTIMATE THE SEVERE RESPIRATORY SYNCYTIAL VIRUS PNEUMONIA IN CHILDREN BASED ON MACHINE LEARNING. Shock 2025;63(4):533 View
  5. Lei M, Liu X, Cheng L, Li Y, Tang N, Song J, Song M, Su Q, Liu M, Fu S, Sun B, Gao Y. An ensemble machine learning-based risk stratification tool for 30-day mortality prediction in critically ill cardiovascular patients. Cardiovascular Diabetology 2025;24(1) View
  6. Zeng S, Cao Z, Xu H, Yang C, Wang K, Yang Y, Qiu X, Xiao Y, Zhang X, Fu Q, Wang W. An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy. BioMedical Engineering OnLine 2025;24(1) View
  7. Zhang Z, Geng X, Yin M, Liang Y, Zheng G. Establishment and validation of a diagnostic model for cholangiocarcinoma based on LightGBM machine-learning algorithm. Scientific Reports 2025 View
  8. Saimaiti X, Li Y, Aikebaier Y, Dong X, Cui H, Hu Y, Yang J. Development and validation of a nomogram model for predicting in-hospital cardiac arrest risk: A prospective multi-center observational study. Medicine 2025;104(49):e46389 View