Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study.

Koutarou Matsumoto,Yasunobu Nohara,Mikako Sakaguchi,Yohei Takayama, Syota Fukushige,Hidehisa Soejima,Naoki Nakashima,Masahiro Kamouchi

JMIR perioperative medicine（2023）

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摘要

The XGBoost model did not significantly outperform the LASSO model in predicting postoperative delirium. Furthermore, a parsimonious logistic model with a few important predictors achieved comparable performance to machine learning models in predicting postoperative delirium.

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关键词

postoperative delirium,machine learning models,electronic health record data,electronic health record,machine learning

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