[The development of model of prognostication and minimization of risk of by-effects under combined application of agents for treatment of chronic cardiac deficiency using AI].

The chronic cardiac deficiency continues to be one of the leading health care problems requiring innovative solutions. The article presents mathematical algorithm to evaluate drug interactions and targeted to minimize side effects and to optimize chronic cardiac deficiency therapy. The mathematical model, elaborated using AI, is based on analysis of fully connected sub-graphs and ranking of side effects of combined application of medications. This approach permits to implement optimal selection of the safest and most effective combinations of medications. This is particularly important with regard for co-morbid conditions when patients take simultaneously several different medications. The proposed approach can significantly improve risk prediction and favor more precise selection of combined therapy. The algorithm surmises necessity for further extension and specification of model, including consideration of wider spectrum of medications and mechanism of their interaction. In the context of rapidly advancing digital medicine, models based on mathematical algorithms and machine learning can complement systems of clinical decision support. These models also can become valuable tool improving treatment of various diseases, especially in co-morbid conditions opening new horizons in medical practice.