Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50895, first published .
Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study

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

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

Koutarou Matsumoto   1 , MPH, PhD ;   Yasunobu Nohara   2 , PhD ;   Mikako Sakaguchi   3 , BSN ;   Yohei Takayama   3 , MS ;   Syota Fukushige   4 , MS ;   Hidehisa Soejima   5 , MD, PhD ;   Naoki Nakashima   6 , MD, PhD ;   Masahiro Kamouchi   7, 8 , MD, PhD

1 Biostatistics Center, Kurume University, Kurume, Japan

2 Big Data Science and Technology, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

3 Department of Nursing, Saiseikai Kumamoto Hospital, Kumamoto, Japan

4 Department of Inspection, Saiseikai Kumamoto Hospital, Kumamoto, Japan

5 Institute for Medical Information Research and Analysis, Saiseikai Kumamoto Hospital, Kumamoto, Japan

6 Medical Information Center, Kyushu University Hospital, Fukuoka, Japan

7 Department of Health Care Administration and Management, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

8 Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

Corresponding Author: