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

Artificial Intelligence (2129) Clinical Information and Decision Making (1915) Clinical Informatics (1325) Cardiac Risk and Cardiac Risk Calculators (102) Machine Learning (2076) mHealth in a Clinical Setting (743) Advanced Data Analytics in eHealth (209) Perioperative Software Tools for Clinicians (19) Electronic Health Records (1319)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66366, first published .
Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Authors of this article:

Ju-Seung Kwun1, 2 Author Orcid Image ;   Houng-Beom Ahn1, 2 Author Orcid Image ;   Si-Hyuck Kang1, 2 Author Orcid Image ;   Sooyoung Yoo3 Author Orcid Image ;   Seok Kim3 Author Orcid Image ;   Wongeun Song3, 4 Author Orcid Image ;   Junho Hyun5 Author Orcid Image ;   Ji Seon Oh6 Author Orcid Image ;   Gakyoung Baek7 Author Orcid Image ;   Jung-Won Suh1, 2 Author Orcid Image
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Journals

  1. Cong X, Zou X, Zhu R, Li Y, Liu L, Zhang J. Development and validation of a machine learning-based model for perioperative stroke prediction in noncardiac, nonvascular, and nonneurosurgical patients. Frontiers in Physiology 2025;16 View