Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery

Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery

Abstract Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying...

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Bibliographic Details
Main Authors: Tomohisa Seki, Toru Takiguchi, Yu Akagi, Hiromasa Ito, Kazumi Kubota, Kana Miyake, Masafumi Okada, Yoshimasa Kawazoe
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Perioperative risk
Machine learning
Iterative random forests
Non-cardiac surgery
Online Access:https://doi.org/10.1038/s41598-024-78482-4
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