Adaptive Learning Systems for Data Conversion in EHRs through Machine Learning

Abstract

Healthcare data management has advanced with Electronic Health Records (EHRs), enhancing the efficiency of medical procedures. Machine learning applied to EHRs transitions healthcare from reactive to proactive, supporting the cost-efficiency and sustainability goals of smart cities. However, digitizing medical records introduces security risks, especially from internal threats, necessitating strong detection systems. Research into machine learning techniques, such as decision trees, random forests, and support vector machines (SVMs), shows their effectiveness in detecting EHR breaches. Balancing system usability with patient privacy remains a key challenge amid widespread data sharing. This study highlights SVMs and deep learning models as promising for improving EHR data accuracy, enhancing detection efficiency, and supporting clinical decisions. Despite advancements in AI, deep learning continues to play a crucial role in refining clinical decision systems, including translating EHR data using technologies like natural language processing (NLP). The study provides a qualitative analysis of how deep learning can optimize EHR processes while addressing security and functional challenges.