AI and the World's Hard Problems · Medicine & drug discovery · Nature (computational biology) · 2026-10-25
Interpretable machine learning for predicting intraoperative oxygenation impairment during one-lung ventilation in thoracic surgery: a retrospective cohort study
This study tested five machine learning models on data from 519 thoracic surgery patients to predict oxygenation impairment during one-lung ventilation. The random forest model performed best (AUC 0.924), ahead of logistic regression (0.790). SHAP analysis identified positive end-expiratory pressure, preoperative arterial oxygen partial pressure, and body mass index as the main predictors.