Predictive Modeling of Disease Progression Using Deep Learning on Multimodal Medical Data
Keywords:
Deep Learning, Multimodal Data, Alzheimer’s Disease, Convolutional Neural Networks, Predictive Modeling, ADNIAbstract
Early detection and monitoring of neurodegenerative diseases such as Alzheimer’s disease (AD) is crucial for improving treatment outcomes and patient care. Traditional diagnostic approaches rely heavily on clinical symptoms, which often appear after significant disease progression. Recent advances in deep learning (DL) provide new opportunities for integrating multimodal data—including neuroimaging, genomics, and electronic health records (EHRs)—to improve predictive accuracy and provide personalized risk assessments. This study develops convolutional neural network (CNN)-based architectures to analyze multimodal datasets for predictive modeling of Alzheimer’s progression. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we integrate magnetic resonance imaging (MRI), genomic biomarkers (e.g., APOE genotype), and longitudinal EHRs. Cross-validation experiments demonstrate that the proposed multimodal CNN achieves an overall accuracy of 91.2% in predicting disease progression, outperforming unimodal approaches by 14–19%. These findings highlight the potential of deep learning-driven multimodal integration for early disease detection and prognosis in neurodegenerative disorders.
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