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

Artificial Intelligence in Surgery and Perioperative Medicine (29) Machine Learning (1535) Postoperative Monitoring and Telemonitoring (131) Artificial Intelligence (1479) Clinical Information and Decision Making (1465) Decision Support for Health Professionals (1232)

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

Authors of this article:

Koutarou Matsumoto1 Author Orcid Image ;   Yasunobu Nohara2 Author Orcid Image ;   Mikako Sakaguchi3 Author Orcid Image ;   Yohei Takayama3 Author Orcid Image ;   Syota Fukushige4 Author Orcid Image ;   Hidehisa Soejima5 Author Orcid Image ;   Naoki Nakashima6 Author Orcid Image ;   Masahiro Kamouchi7, 8 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Holler E, Ludema C, Ben Miled Z, Rosenberg M, Kalbaugh C, Boustani M, Mohanty S. Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study. JMIR Perioperative Medicine 2025;8:e59422 View