Advanced Framework for Multi-Modal Healthcare Data Integration: Leveraging HPC with GPU Computing and CNN Architecture in CDSS

Abstract

In this study, we shall be looking at the challenges involved in integrating multi-modal healthcare data in the clinical decision support systems (CDSS). We propose the Automated Multi-Modal Data Integration (AMMI-CDSS) algorithm, which will utilize the latest high-performance computing (HPC) techniques such as the Convolutional Neural Network (CNN) architecture and the Graphics Processing Unit (GPU) computing to provide precise and rapid analysis. Which features will be extracted, multi-modal data will be merged, data will be prepared and algorithms developed in a distributed computing environment. We illustrate how AMMI-CDSS through the use of real world datasets such as wearable sensors data, medical imaging, genetic data, and electronic health records (EHRs), can improve the clinical decision support. By performing harmonization of the diverse data sources into a unique dataset after thorough data preprocessing and complex calculations, AMMI-CDSS provides the analysis with better quality and coherence. Our study allow us to make conclusion about how HPC-based CDSS models can be compared to conventional machine learning ones using their scalability and performance as key metrics. We enrich CDSS with the methodical framework for one-by-one testing and evaluation of proposed models and multi-modal healthcare data analysis. Future research might explore novel methods for integrating diverse types of healthcare data, as well as enhancing the НРС-based CDSS models by keeping them up-to-date.