A Comprehensive Review of Methods Used for Health Prediction and Monitoring Utilizing an Electronic Medical Records (EMR) System

Abstract

In the rapidly evolving field of healthcare, Artificial Intelligence (AI) and pattern recognition play a key role in enhancing disease diagnosis and prediction. As the patient population increases, the digitalization of medical records has become essential, therefore electronic medical records were developed. This stored Electronic Medical Records (EMR) data can be used to predict possible diseases based on the symptoms stored in the system. This study delves into the integration of AI methodologies within EMR systems, providing a comprehensive review of current techniques that have been used in health prediction and monitoring using EMR data. In this paper, different AI-driven approaches were examined and compared, including Deep Learning (DL), Machine Learning (ML), and Rule-Based Methods. This paper reveals the potential of these techniques in accurately diagnosing diseases, additionally, it discusses challenges and future directions, emphasizing the need for innovative solutions to optimize EMR systems in the context of AI and pattern recognition. Several instances where AI models, such as the application of Support Vector Machine (SVM) models, achieved predictive accuracies of 86.2% and 97.33% in different cancer types, and ML models diagnosing Diabetic Retinopathy with a 92% accuracy rate were observed. Variations in the effectiveness of these technologies across different diseases were also observed, such that a technique that has high accuracy in one disease may have lower accuracy in a different disease. This paper aims to contribute to the growing body of knowledge in AI applications in healthcare, offering insights into the development of more efficient, accurate, and predictive healthcare models.