Keywords: Diagnosis/Prediction, Stroke, Stroke occurence prediction; Deep learning
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.
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