Machine learning predicts postoperative delirium risk in general surgery patients, suggesting tailored interventions may enhance outcomes.
This study employed machine learning to predict postoperative delirium in general surgery patients using data from 2017-2023. Pre-surgical, surgical, and post-surgical variables linked to delirium risk were identified through data analysis. The XGBoost model demonstrated superior performance, achieving an accuracy of 0.92 and an AUROC of 0.94. The model offers potential for early identification of high-risk patients, facilitating personalized interventions and improved outcomes.
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Park et al. (2025) studied this question.
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