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

Machine Learning (2308) Big Data (320) Decision Support for Health Professionals (1694) Postoperative Monitoring and Telemonitoring (214) Postoperative Cognitive Dysfunction (POCD) (9) Cardiac Surgery (25) Perioperative Risk Assessment (35)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73283, first published .
Surgeons in sterile blue gowns perform a surgical procedure with medical monitoring equipment.

Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation

Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation

Authors of this article:

Huixiu Hu1, 2, 3 Author Orcid Image ;   Yuxiang Wang4 Author Orcid Image ;   Houfeng Li5 Author Orcid Image ;   Qinglai Zang6, 7 Author Orcid Image ;   Jing Huang8 Author Orcid Image ;   Ying Zhang3 Author Orcid Image ;   Jinjing Wu4 Author Orcid Image ;   Long Liu4 Author Orcid Image ;   Zhen Xing3 Author Orcid Image ;   Yaohua Yu1, 2 Author Orcid Image
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Journals

  1. Li C, Zhang T, Chen H, Wang S, Wang L, Huan Y, Liu J, Liu L. Early prediction of incident delirium in traumatic brain injury: a multicenter validated and interpretable machine learning approach. Frontiers in Neurology 2026;17 View