This paper proposes a novel hybrid architecture, MultiModal-CDSS, that extends traditional Clinical Decision Support Systems to integrate multi-modal patient data including medical imaging, clinical notes, genomic profiles, and wearable sensor data. The framework employs hierarchical multi-modal fusion strategy with modality-specific encoders, cross-modal attention mechanisms, and a hybrid reasoning layer combining deep learning with knowledge-based clinical rules.
Key findings
Proposes MultiModal-CDSS, a hybrid architecture integrating diverse patient data modalities.
Employs modality-specific encoders and cross-modal attention mechanisms for data fusion.
Combines deep learning with knowledge-based clinical rules for hybrid reasoning.
Addresses challenges like temporal misalignment, missing modalities, data heterogeneity, and clinical interpretability.
Limitations & open questions
The proposed system requires extensive validation across multiple disease domains.
Real-world deployment scenarios and system robustness need further evaluation.