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

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
  2. Friedman J, Parchure P, Cheng F, Fu W, Cheertirala S, Timsina P, Raut G, Reina K, Joseph-Jimerson J, Mazumdar M, Freeman R, Reich D, Kia A. Machine Learning Multimodal Model for Delirium Risk Stratification. JAMA Network Open 2025;8(5):e258874 View
  3. Schöler L, Graf L, Airola A, Ritzi A, Simon M, Peltonen L. Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review. JAMIA Open 2025;8(3) View