Hybrid Intelligent Systems for a Personalized Assessment of the State of Complex Medical Objects Using Dynamic Object Modeling

This paper describes specific problems in the assessment of functions of diagnosed complex objects in medicine and the risk of defects to make a complete differential diagnosis and predict the state of these objects and proposes to develop and investigate methods and models of a new class of hybrid artificial intelligence systems for medical diagnostics and prediction. These systems are to integrate heterogeneous knowledge of the diagnostic process, the prediction of outcomes, and the interpretation of the prediction of identified dysfunctions and pathologies. It is proposed to combine models including (1) the functional and transformational models of hybrid intelligent systems of two types and (2) the model of complex object functioning as a heterogeneous biological system.