DEEP LEARNING BASED PREDICTIVE ANALYTICS USING ELECTRONIC HEALTH RECORDS: CURRENT APPLICATIONS, IMPLEMENTATION CHALLENGES, AND FUTURE DIRECTIONS - Malik, Syed Mohd Faisal; Nafis, Md. Tabrez; Ahad, Mohd Abdul; Tanweer, Safdar; tauhid, SM Faizanut (ShodhKosh: Journal of Visual and Performing Arts, 2024)
The increasing digitization of healthcare systems has led to the widespread adoption of Electronic Health Records (EHRs), offering a vast and rich source of longitudinal patient data. In recent years, the emergence of deep learning techniques has significantly enhanced the capacity for predictive modelling using such data. Deep learning models have demonstrated superior performance in capturing complex, non-linear relationships across diverse clinical variables, thereby enabling more accurate forecasts of disease progression, hospital readmissions, treatment responses, and other critical outcomes. This review critically examines the current landscape of deep learning applications in predictive analysis based on EHRs, identifying key technological advancements and practical implementations in various clinical contexts. It further explores the multidimensional challenges that hinder widespread deployment, including data quality issues, model interpretability, ethical concerns, and integration into clinical workflows. The discussion underscores the necessity for interdisciplinary collaboration, standardized data frameworks, and the development of transparent and privacy-preserving AI models to ensure equitable and responsible use of deep learning in healthcare. This paper aims to provide a comprehensive perspective that will inform future research directions and policy-making in the development of intelligent, patient-centered healthcare systems.