Predicting Antiseizure Medication Outcomes in Early Diagnosed Epilepsy: A Multimodal Framework Using EEG, MRI, and Clinical Data
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Accurate prediction of antiseizure medication (ASM) outcomes is crucial for optimising epilepsy treatment. We propose a multi-modal deep learning framework that integrates electroencephalography (EEG), magnetic resonance imaging (MRI), clinical factors, and molecular drug features to enhance ASM outcome prediction. Our approach includes EEG Q-Net, a pre-trained quantisation model capturing finegrained temporal EEG patterns, and MRI embedding from BiomedCLIP. To capture chemical similarities and interactions for the ASMs, we employed a pre-trained model trained on thousands of medications to generate embedding for SMILES (Simplified Molecular Input Line Entry System). Our findings underscore the benefits of multimodal integration for personalised epilepsy management, as our fusion model achieved an AUC of 0.75, an improvement over the best unimodal model (0.71).
