| Title |
Multistage analysis method for detection of effective herb prescription from clinical data
|
|---|---|
| Published in |
Frontiers of Medicine, June 2017
|
| DOI | 10.1007/s11684-017-0525-8 |
| Pubmed ID | |
| Authors |
Kuo Yang, Runshun Zhang, Liyun He, Yubing Li, Wenwen Liu, Changhe Yu, Yanhong Zhang, Xinlong Li, Yan Liu, Weiming Xu, Xuezhong Zhou, Baoyan Liu |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 24 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 21% |
| Other | 2 | 8% |
| Student > Ph. D. Student | 2 | 8% |
| Unspecified | 1 | 4% |
| Student > Doctoral Student | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 11 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 3 | 13% |
| Medicine and Dentistry | 2 | 8% |
| Unspecified | 1 | 4% |
| Computer Science | 1 | 4% |
| Business, Management and Accounting | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 14 | 58% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 June 2017.
All research outputs
#18,555,330
of 22,981,247 outputs
Outputs from Frontiers of Medicine
#223
of 351 outputs
Outputs of similar age
#242,185
of 317,509 outputs
Outputs of similar age from Frontiers of Medicine
#9
of 14 outputs
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