Optimized Multi-Modal Healthcare Data Integration: Harnessing HPC and GPU-Accelerated CNNs for Enhanced CDSS

The mixing of multi-modal healthcare information is critical for enhancing clinical decision support systems (CDSS) by means of leveraging various data assets, consisting of electronic health information (EHRs), medical imaging, and wearable sensor information. however, traditional device studying fashions hostilities to efficiently method and examine such heterogeneous datasets because of their complexity, excessive dimensionality, and interoperability challenges. To address those boundaries, we advocate the automatic Multi-Modal records Integration (AMMI-CDSS) framework, a High-performance computing (HPC)-based totally technique that makes use of GPU-improved deep learning models for actual-time, large-scale healthcare facts analysis. The AMMI-CDSS framework implements a multi-stage pipeline encompassing facts pre-processing, characteristic extraction, multi-modal information fusion, and deep learning-based predictive modelling. The proposed machine employs Convolutional Neural Networks (CNNs) for clinical image feature extraction, long brief-term memory (LSTM) networks for time-collection wearable sensor records, and multi-modal transformers for move-modal getting to know, all optimized thru HPC and parallel GPU computing. Comparative experiments demonstrate that GPU-based hybrid deep learning fashions drastically outperform traditional CPU-based totally techniques, reaching better accuracy, precision, recall, and computational performance in tasks which include ECG type and pores and skin cancer detection. The AMMI-CDSS device no longer only complements real-time scientific selection-making however also improves ailment analysis, risk prediction, and affected person monitoring. by way of integrating multi-supply healthcare records within a unified framework, AMMI-CDSS facilitates personalized medicine, reducing diagnostic mistakes and optimizing remedy techniques. This studies highlights the crucial function of excessive-performance computing, deep mastering, and multi-modal records fusion in reworking current healthcare analytics. future studies will awareness on improving model interpretability, integrating federated studying for privacy-retaining AI, and increasing actual-time selection assist capabilities in CDSS programs.