AUTOMATIC DISEASE DETECTION BASED ON ELECTRONIC HEALTH RECORDS

Abstract

This article explores the development and potential applications of algorithms designed for the automatic detection of diseases using electronic medical records (EMRs). EMRs provide a comprehensive digital platform for storing and analyzing patient health data. The study investigates contemporary methods for disease recognition through advanced technologies such as machine learning, artificial intelligence, and natural language processing (NLP). Furthermore, it assesses the performance of these algorithms in terms of accuracy, sensitivity, and clinical relevance. The findings demonstrate that EMR-based automated systems significantly contribute to improving clinical decision-making and facilitating early diagnosis. The paper concludes with an overview of current challenges and future opportunities for integrating these technologies into healthcare systems.