Prediction of Lifetime Acute Ischemic Stroke Risk using Multimodal Models and SHAP-based Interpretability Methods

Motivation: Accurately predicting the lifetime risk of acute ischemic stroke (AIS) remains a significant challenge, and there is a notable scarcity of multimodal models that effectively integrate medical imaging with clinical factors. Goal(s): To propose an effective multi-modality deep learning model based on both MR images and clinical factors for improved prediction of AIS occurrence. Approach: The model leveraged a clinical-factor-agnostic module to extract clinical features from clinical factors and employed Shapley methods to scrutinize the significance of features. Results: The proposed method achieved higher performance than conventional models for the prediction of lifetime AIS occurrence. Impact: Our model's predictive outcomes could pinpoint individuals at high risk for AIS, allowing clinicians to advise them on self-health vigilance.