Abstract
The model for ranking side effects in the combined use of drugs is constructed using the example of chronic heart failure. A numerical algorithm based on the allocation of fully connected subgraphs has been developed, which reduces the amount of calculations when analyzing combinations of several drugs. The results of the test calculations are presented. The program being developed can be useful as a medical decision support system.
Publisher
Keldysh Institute of Applied Mathematics
Reference7 articles.
1. Feixiong Cheng, Zhongming Zhao. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties // J. Medicine Information Association, 2014, 21, p. 278-286.
2. Tengfei Lyu, Jianliang Gao, Ling Tian, Zhao Li, Peng Zhang and Ji Zhang A Multimodal Deep Neural Network for PredictingDrug-Drug Interaction Events // Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), p. 3536-3542.
3. Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M Lin, Wen Zhang, Ping Zhang and Huan Sun Graph embedding on biomedical networks: methods, applications and evaluations // Bioinformatics, 2020, 36(4), p. 1241– 1251.
4. Xu Chu, Yang Lin, Yasha Wang, Leye Wang, Jiangtao Wang, and Jingyue Gao A multi-task semi-supervised learning framework for drug-drug interaction prediction. // Proceedings of the International Joint Conference on Artificial Intelligence, 2019, p. 4518-4524.
5. Farkas D., Shader R.I., von Moltke L.L., Greenblatt D.J. Mechanisms and consequences of drug-drug interactions. // In: Gad SC, ed. Preclinical Development Handbook: ADME and Biopharmaceutical Properties. Philadelphia: Wiley, 2021.