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
- DOI
- 10.2196/91315
- Published
- 2026-10-21
- Container
- JMIR Perioperative Medicine
- Publisher
- JMIR Publications Inc.
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2196/91315,
title = {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},
author = {Takuya Nishino and Hiroshi Mase and Chol Kim and Shinji Sugita and Yukihiro Kondo and Masashi Ishikawa},
year = {2026},
journal = {JMIR Perioperative Medicine},
doi = {10.2196/91315},
url = {https://doi.org/10.2196/91315}
}RIS
TY - JOUR TI - 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 AU - Takuya Nishino AU - Hiroshi Mase AU - Chol Kim AU - Shinji Sugita AU - Yukihiro Kondo AU - Masashi Ishikawa PY - 2026 JO - JMIR Perioperative Medicine DO - 10.2196/91315 UR - https://doi.org/10.2196/91315 ER -
APA
Nishino, T., Mase, H., Kim, C., Sugita, S., Kondo, Y., & Ishikawa, M. (2026). 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. JMIR Perioperative Medicine. https://doi.org/10.2196/91315
Source records
- crossref · retrieved 2026-11-10T11:30:04.244Z