Abstract:
The paper formulates a model for assessing the risk of consequences from taking several medicines within the framework of an approach using intelligent processing of instructions for the use of drugs. The model also uses rank estimates of the relative severity of side effects and similar pharmacodynamics when taking several drugs. Using the example of the treatment of chronic heart failure, the use of this model as a basis for the development of a system to support medical decision-making in the field of polypharmacotherapy is shown. The assignment of the rank to the "medicine – consequence" pair is carried out taking into account the frequency of side effects, gender and age of the patient. The model allows for automatic correction of ranks within the framework of a personalized approach, when the severity of consequences increases if the patient has appropriate concomitant diseases. The compatibility of several drugs is determined on the basis of a graph model, when only those vertices are connected to each other (i.e. drugs), for which the total rank of consequences for each of the possible side effects is less than the critical level taken as one in the rank model. Therefore, the task of drug compatibility is reduced to the allocation of fully connected subgraphs, since a necessary, although not sufficient, condition for the compatibility of n drugs is their pairwise compatibility. This approach makes it possible to significantly reduce the computational complexity of the task, since clustering at the level of complete connectivity reduces the dimensionality of the possible number of drugs used simultaneously.
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