Integrating Deep Learning And EHR Analytics For Real-Time Healthcare Decision Support And Disease Progression Modeling

Abstract

Background: Growing health care data is requiring advanced solutions for healthcare clinical decision-making; EHR analytics fused with deep learning addresses the growing need by providing data-driven insights and predictive analytics to improve real-time patient management of disease progressions. Objectives: This research will work to develop a deep learning-based framework that combines EHR analytics for real-time clinical decision support, disease progression modeling, and personalized treatment recommendations for better healthcare outcomes and operational efficiency. Methods: The data was collected from both EHRs and wearable devices. CNNs and RNNs models were used in the process of disease progression modeling. Preprocessing and alignment of temporal features helped in proper insight generation. Results: The proposed system improved the accuracy in diagnosis to 85% and fine-tuned the predictive insights of disease progression and advocated personalized treatments. This gave the clinicians actionable information about early risk detection and, therefore, contributed to an effective healthcare delivery system. Conclusion: This is underpinned by high-level integration through AI-based decision support and disease modeling, but there are lots of privacy and security issues.