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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Hyungbok Lee¹, Taesa Ahn, Sohyeon Park

¹Nursing Department, Seoul National University Hospital, Seoul, Republic of Korea.

Computers, Informatics, Nursing : CIN

|February 2, 2026

Summary

This summary is machine-generated.

Researchers identified key risk factors for postoperative delirium in general hospital patients. Machine learning models can predict this complication using electronic health records, improving patient care.

Keywords:

Explainable artificial intelligence Machine learning Postoperative delirium Prediction model

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Geriatric Medicine

Background:

Postoperative delirium is a common and severe complication in surgical patients.
Current prediction tools for general ward patients are insufficient.
Early identification of at-risk patients is crucial for timely intervention.

Purpose of the Study:

To identify significant risk factors for postoperative delirium in non-intensive care unit (non-ICU) patients.
To develop and evaluate a machine learning model for predicting postoperative delirium.
To leverage electronic health record (EHR) data for improved clinical decision-making.

Main Methods:

Retrospective analysis of 85,884 surgical patients from 2017-2022.
Utilized 53 potential predictor variables from EHR data.
Employed machine learning algorithms, with LightGBM demonstrating optimal performance.

Main Results:

Higher comorbidity count, advanced age, increased drain count, elevated sodium, and decreased albumin levels were identified as key risk factors.
Age was the most significant predictor across most surgical specialties.
Intensive care unit (ICU) transfer emerged as a critical factor specifically in neurosurgery.

Conclusions:

An AI-based predictive model using EHR data can effectively identify patients at risk for postoperative delirium.
The findings provide a foundation for developing targeted interventions to mitigate delirium risk.
This approach has the potential to enhance patient care and outcomes in general surgical wards.