がん患者における術後合併症の予測因子としての身体活動トレンド：機械学習アプローチ

Early detection of postsurgical complications is critical for improving surgical outcomes, yet current monitoring methods are invasive, time-consuming, and may delay intervention. Advances in machine learning (ML) and artificial intelligence (AI) enable using real-time data, such as accelerometer-derived physical activity, as potential early warning signs. This exploratory study evaluated whether activity trends can predict postsurgical complications in oncology patients using ML models.

Usual care data from a surgical oncology ward (October 2020-December 2024) were analyzed. Three classifiers were evaluated-Random Forest (RF), eXtreme Gradient Boosting (XGB), and Logistic Regression (LR)-within a nested cross-validation framework. Two modeling strategies were compared: (1) training/testing without undersampling and (2) training with undersampling at varying factors to balance complication versus noncomplication days. Models were assessed for next-day complication prediction using area under the ROC curve (AUC), precision, recall, and F1-score with bootstrap confidence intervals.

Data were collected from 965 patients, of whom 189 were included. The best performance for RF was observed at an undersampling factor of 1 (AUC = 0.66, 95% confidence interval (CI) 0.64-0.67; recall = 0.63, 95% CI 0.27-0.91; precision = 0.05, 95% CI 0.03-0.07). LR achieved its highest AUC without undersampling (0.68, 95% CI 0.67-0.69), while XGB performed consistently lower (AUC ≈ 0.63-0.64).

This exploratory study showed that postoperative activity trends alone were insufficient to predict complications after major oncological surgery. Combining accelerometer, physiological, and laboratory data may improve predictive accuracy and overall clinical value in perioperative care.