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

Outcomes Research in Perioperative Medicine (77) Clinical Informatics (2397) Machine Learning (3346) Postoperative Monitoring and Telemonitoring (263) New and Emerging Methods and Tools in Surgery and Anesthesiology (76)

Published on in Vol 9 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/91315, first published .
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Machine Learning–Based Prediction of Major Postoperative Complications After Noncardiac Surgery Under General Anesthesia Using Routinely Available Electronic Health Record Data: Retrospective Multicenter Development and Validation Study

Machine Learning–Based Prediction of Major Postoperative Complications After Noncardiac Surgery Under General Anesthesia Using Routinely Available Electronic Health Record Data: Retrospective Multicenter Development and Validation Study

Authors of this article:

Takuya Nishino1 Author Orcid Image ;   Hiroshi Mase2, 3 Author Orcid Image ;   Chol Kim4 Author Orcid Image ;   Shinji Sugita5 Author Orcid Image ;   Yukihiro Kondo1, 6 Author Orcid Image ;   Masashi Ishikawa2 Author Orcid Image
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