AUTOMATIC DISEASE DETECTION BASED ON ELECTRONIC MEDICAL RECORDS

This article examines the development and application possibilities of algorithms for automatic disease detection based on electronic medical records (EMRs). EMRs serve as a primary source that enables the storage and analysis of patient health information in digital format. The paper explores modern approaches to disease identification using advanced technologies such as machine learning, artificial intelligence, and natural language processing (NLP). It also evaluates the accuracy, sensitivity, and applicability of these algorithms in clinical practice. Research findings indicate that automated systems based on EMRs play a crucial role in enhancing the efficiency of clinical decision-making and ensuring early diagnosis. The article concludes with a discussion of the prospects and existing challenges of implementing these technologies in healthcare systems.