Exploration on rationality evaluation approach of drug combination medication based on sequential analysis and machine learning.
10.19540/j.cnki.cjcmm.20210315.601
- Author:
Zhen-Zhen WANG
1
;
Shuang LIU
2
;
Jiang-Ling LI
3
;
Xiao-Fang WANG
4
;
Ai-Ting WANG
2
;
Ke-Jun DENG
5
;
Hao LIN
5
;
Dan YAN
2
Author Information
1. School of Traditional Chinese Medicine, Shenyang Pharmaceutical University Benxi 117004, China.
2. Beijing Friendship Hospital, Capital Medical University Beijing 100050, China Beijing Key Laboratory of Bio-characteristic Profiling for Evaluation of Rational Drug Use Beijing 100038, China.
3. Beijing Friendship Hospital, Capital Medical University Beijing 100050, China School of Pharmacy, Chengdu University of Traditional Chinese Medicine Chengdu 611137, China.
4. Beijing Key Laboratory of Bio-characteristic Profiling for Evaluation of Rational Drug Use Beijing 100038, China Beijing Shijitan Hospital, Capital Medical University Beijing 100038, China.
5. Center for Informational Biology, University of Electronic Science and Technology of China Chengdu 611731, China.
- Publication Type:Multicenter Study
- Keywords:
drug combination;
machine learning;
rationality evaluation;
sequential analysis
- MeSH:
Artificial Intelligence;
Drug Combinations;
Machine Learning;
Technology
- From:
China Journal of Chinese Materia Medica
2021;46(9):2356-2362
- CountryChina
- Language:Chinese
-
Abstract:
Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluation that reflects its clinical characteristics. In order to break through the limitations of existing evaluation tools, examining drug-to-drug and drug-to-target action characteristics is proposed from the physical, chemical and biological perspectives, combining clinical multicenter case resources, domestic and international drug interaction public facilities with the aim of discovering the common rules of drug combination. Machine learning technology is employed to build a system for evaluating and predicting the rationality of clinical drug combinations based on "drug characteristics-repository information-artificial intelligence" strategy, which will be debugged and validated in multi-center clinical practice, with a view to providing new ideas and technical references for the safety and efficacy of clinical drug use.
