Virtual Training Simulator and Medical Decision Support for Personalized Assessment of Severity and Prediction of the Patients' State (the Case of Acute Pancreatitis): Investigation of the Problem Area and Identification of the Problem

Multimorbid, polyetiological diseases (including acute pancreatitis) have a diverse and often unpredictable clinical picture intersecting with a number of other diseases. They are often characterized by a phased course and a high probability of developing severe complications and mortality. This changes and complicates the clinical picture and clinical course of the disease as well as the process of personalized assessment of severity and prediction of the condition of a complex object in medicine (the patients' body). As a result, education, training and advanced training in this area are also complicated. In order to improve the quality of medical intelligent and training systems, a new class of intelligent systems is proposed, namely cooperative self-adjusting hybrid intelligent systems ”Virtual councils for personalized assessment of severity and prognosis of patients' condition” (the case of acute pancreatitis). They are supposed to be developed within the framework of the problem-instrumental methodology of Professor A.V. Kolesnikov et al. This paper presents the results of the first stage of this methodology, namely: the results of an analysis of the specifities of the problematic environment for the diagnostic and prognostic assessing of the state of the patients' body using the example of acute pancreatitis; a functional model of the decision-making process regarding the problem of the personalized assessment of severity and predicting the condition of a patient with suspected acute pancreatitis as well as the results of its identification.