Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.

Background: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategies.

Methods: A multicenter study was conducted using data collected from 718 patients with aSAH who were treated at five hospitals in Japan. A deep learning model was developed to predict outcomes based on modified Rankin Scale scores using pretherapy clinical data collected from admission to the initiation of physical therapy. The model's performance was assessed using the area under the curve, and interpretability was enhanced using SHapley Additive exPlanations (SHAP). Logistic regression analysis was also performed for further validation.

Results: The area under the receiver operating characteristic curve of the model was 0.90, with age, World Federation of Neurosurgical Societies grade, and higher brain dysfunction identified as key predictors. SHAP analysis supported the importance of these features in the prediction model, and logistic regression analysis further confirmed the model's robustness.

Conclusions: The novel deep learning model demonstrated strong predictive performance in determining functional outcomes in patients with aSAH, making it a valuable tool for guiding early rehabilitation strategies.