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

Artificial Intelligence (1214) Machine Learning (1400) Cardiac Risk and Cardiac Risk Calculators (32) Decision Support for Health Professionals (1145)

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 (2) Tweetations (3) Metrics

Journals

  1. 夏来百提姑 ·. Research Progress of In-Hospital Cardiac Ar-rest Early Warning Model Based on Machine Learning. Advances in Clinical Medicine 2024;14(01):871 View
  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