Purpose. To increase the accuracy and efficiency of diagnosing a patient’s condition in a clinical setting, which ensures increased efficiency of treatment and prevention of complications of the patient’s condition.
Specifications. The information technology was developed using the Data Miner module procedure of the STATISTICA 10 package and the use of a modern programming tool – Python.
Application area. Healthcare. Health care institutions at various levels.
Advantages. Today, the use of Data Mining methods in the analysis of clinical data to support the activities of a doctor is becoming widespread. Most of the analogues under consideration use separate Data Mining methods, in particular clustering methods, which does not provide a comprehensive and personalized analysis of data on the dynamics of the patient’s condition. The created technology combines various Data Mining methods, which increases the efficiency of the diagnostic and treatment processes, which leads to the quality of medical care in clinical healthcare institutions.
Technical and economic effect. Improving the efficiency and quality of medical care for patients in clinical settings.
Description. The created technology relates to applied artificial intelligence systems in the healthcare sector to support the activities of a doctor. Based on the developed qualification models, a set of predictors of condition deterioration was identified, which form the basis for the development of decisive rules for classifying a particular patient’s membership in a typological group according to the severity of his condition.