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

Artificial Intelligence (4528) Clinical Informatics (2132) Clinical Information and Decision Making (3527) mHealth in a Clinical Setting (1099) Electronic Health Records (1656) Advanced Data Analytics in eHealth (334) Machine Learning (3042) Cardiac Risk and Cardiac Risk Calculators (164) Perioperative Software Tools for Clinicians (29)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66366, first published .
Elderly woman clutching chest in pain, possibly experiencing heart attack symptoms.

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
  2. Han S, Li F, Hou D, Lv X, Zhang H, Xiao S, Lou J, Li H, Cao J, Wang D, Gu X, Peng Y, Xie Y, Feng Y, Wu Q, Xu J, Mi W, Liu Y. Preoperative health status assessed with different scales and postoperative cardiac and cerebrovascular complications in older patients. European Journal of Anaesthesiology 2026 View

Conference Proceedings

  1. Gupta R, Varshney K, Bharati A, Sharma N, Nigam P. 2026 International Conference on Innovations in Computational Intelligence (ICICI). Machine Learning Framework for Postoperative Cardiac Complication Prediction in ENT and Maxillofacial Surgeries View