ARCHITECTURE OF THE INFORMATION SYSTEM SUPPORTING INFORMATION PROCESSING AND DECISION-MAKING IN MEDICINE

This article examines the architecture of an information system that supports information processing and clinical decision-making in the field of medicine. The system is described using a multilayer functional model that enables the integration, analysis, and efficient utilization of heterogeneous data. The utilization of the BioBERT and XGBoost neural network models at data collection, preparation and analysis, as well as decision-making steps using machine learning and expert systems are summarized. The use of IDEF methodology for modeling medical information system is reviewed and the importance of IDEF0, IDEF1, IDEF1X, IDEF2, and IDEF3 models is emphasized. An efficient paradigmatic model for systematic processing of medical information, patient assessment and diagnostics optimality is generated.