Multistage analysis method for detection of effective herb prescription from clinical data.
10.1007/s11684-017-0525-8
- Author:
Kuo YANG
1
;
Runshun ZHANG
2
;
Liyun HE
3
;
Yubing LI
1
;
Wenwen LIU
1
;
Changhe YU
3
;
Yanhong ZHANG
3
;
Xinlong LI
3
;
Yan LIU
4
;
Weiming XU
5
;
Xuezhong ZHOU
6
;
Baoyan LIU
7
Author Information
1. School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
2. Guanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China.
3. Institute of Basic Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
4. Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
5. Institute of Basic Theory of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
6. School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China. xzzhou@bjtu.edu.cn.
7. Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China. liuby@mail.cintcm.ac.cn.
- Publication Type:Journal Article
- Keywords:
core network extraction;
effective prescription detection;
herb set enrichment analysis;
insomnia;
personalized treatment
- MeSH:
Adolescent;
Adult;
Aged;
Aged, 80 and over;
Case-Control Studies;
Child;
China;
Drugs, Chinese Herbal;
therapeutic use;
Female;
Humans;
Male;
Medicine, Chinese Traditional;
Middle Aged;
Propensity Score;
Sleep Initiation and Maintenance Disorders;
drug therapy;
Young Adult
- From:
Frontiers of Medicine
2018;12(2):206-217
- CountryChina
- Language:English
-
Abstract:
Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb-symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.
