Bobyryov Viktor M, Kulishov Sergij K, Vakhnenko Andrij V, Vlasova Olena V
The Higher State Educational Institution of Ukraine, Ukrainian Medical Stomatological Academy, Poltava, Ukraine.
Wiad Lek. 2017;70(6 pt 1):1142-1145.
Purpose of our investigation was to propose and verify the algorithm for making pharmacotherapy decision in the patients with multimorbidity.
Material and methods: Object of investigation: patients with multimorbidity. Observations were conducted according to European Guidelines. We proposed and tested genetic algorithm for making pharmacotherapy decision for such patients. It is necessary to mention, that each person is representing a variant of treating with certain pathology. Chromosome of this variant is composed from genes, where each gene is certain group of drugs. The sequence of solutions of this problem comes down to the selection of drugs for the di-morbid conditions as the descendants of mono-morbidity. At the next stage of selection continues the most successful combinations of drugs for multimorbid conditions as descendants di-morbid and monomorbid conditions. When breeding pairs must take into account the mutual potentiating pathogenic and / or sanogenetic effects.
Results: We had optimal patient's treatment as a result of crossing genes, groups of drugs and obtaining their offspring with the best combination without absolute contraindications and minimal relative contraindications.
Conclusion: Thus, genetic algorithm for making pharmacotherapy decision in the patients with multimorbidity showed effectiveness of drugs choosing.
我们研究的目的是提出并验证用于制定合并多种疾病患者药物治疗决策的算法。
材料与方法：研究对象：合并多种疾病的患者。观察按照欧洲指南进行。我们提出并测试了用于为此类患者制定药物治疗决策的遗传算法。需要提及的是，每个人都代表着某种疾病治疗的一种变体。这种变体的染色体由基因组成，其中每个基因是特定的一组药物。该问题的解决方案序列归结为为合并两种疾病的情况选择药物，作为单一疾病情况的后代。在选择的下一阶段，继续为合并多种疾病的情况选择最成功的药物组合，作为合并两种疾病和单一疾病情况的后代。在配对时必须考虑相互增强的致病和/或促健康效应。
结果：通过基因交叉、药物组以及获得没有绝对禁忌证且相对禁忌证最少的最佳组合的后代，我们得到了患者的最佳治疗方案。
结论：因此，用于制定合并多种疾病患者药物治疗决策的遗传算法显示了药物选择的有效性。
