EARLY DISEASE DETECTION SYSTEM USING ELECTRONIC MEDICAL RECORDS (EMR)
Abstract
Early detection of diseases is critical for improving patient outcomes, reducing healthcare costs, and enabling timely interventions. Electronic Medical Records (EMR) store comprehensive patient data, including demographic information, laboratory results, clinical notes, and medication history, providing a valuable resource for predictive healthcare analytics. Leveraging Artificial Intelligence (AI) and machine learning algorithms on EMR data enables the development of early disease detection systems capable of identifying high-risk patients, predicting disease onset, and supporting clinical decision-making. This thesis examines the role of EMR-based AI systems in early disease detection, their methodologies, benefits, and challenges, and evaluates their impact on precision medicine and healthcare efficiency.
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Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author)
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