PREDICTIVE ANALYTICS IN HEALTHCARE: A REVIEW OF ARTIFICIAL INTELLIGENCE TECHNIQUES USING EHR DATA

Abstract

The advent of electronic health records (EHRs) has revolutionized the healthcare industry by enabling the digitization and systematic organization of patient data. However, the massive volume and complexity of health data present significant challenges for manual analysis and summarization. This review article focuses on three main areas to explore the applications of deep learning (DL) and machine learning (ML) methods in the medical sector. First, it examines the use of EHR data for disease prediction, highlighting significant advancements in supervised learning, unsupervised learning, and various DL architectures. Second, it delves into risk prediction using EHR data, illustrating how ML and DL models are employed to assess and predict patient risks. This section discusses various algorithms and models, such as gradient boosting and recurrent neural networks, used to predict conditions like stroke, atrial fibrillation, and cardiovascular diseases. Lastly, the paper addresses the crucial topic of confidentiality and safety precautions for EHR data. Additionally, it discusses the evaluation metrics used to benchmark these models and examines the challenges and limitations in this domain. Future research directions and emerging trends are also outlined to further enhance the efficacy and adoption of ML and DL in health record management. The purpose of this study is to provide scholars and medical practitioners with a comprehensive overview of the current state and potential future applications of ML and DL for improving health record management.