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

Artificial Intelligence in Surgery and Perioperative Medicine (89) Electronic Health Records (1738) Postoperative Cognitive Dysfunction (POCD) (14) Artificial Intelligence (4811) Perioperative Risk Assessment (55)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59422, first published .
Bald man in hospital bed with oxygen nasal cannula, looking at camera

Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study

Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study

Authors of this article:

Emma Holler1 Author Orcid Image ;   Christina Ludema2 Author Orcid Image ;   Zina Ben Miled3 Author Orcid Image ;   Molly Rosenberg2 Author Orcid Image ;   Corey Kalbaugh2 Author Orcid Image ;   Malaz Boustani4 Author Orcid Image ;   Sanjay Mohanty1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Song Q, Shi P, Tian P, Chao A, Zhang Y, Dong Q. Predicting lower extremity deep vein thrombosis in elderly patients with hip fracture: A machine learning model with emphasis on diabetes as a key risk factor. Experimental and Therapeutic Medicine 2026;32(4):1 View
  2. Huang H, Lu C, Chang G, Tien M, Hsu Y. Prediction models for in-hospital delirium using routinely collected electronic health record data: a systematic review (Preprint). JMIR Medical Informatics 2026 View