Multistage analysis method for detection of effective herb prescription from clinical data

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.

Keywords

effective prescription detection / herb set enrichment analysis / core network extraction / insomnia / personalized treatment

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Nos. 61105055 and 81230086), the National Basic Research Program of China (No. 2014CB542903), the Special Programs of Traditional Chinese Medicine (Nos. 201407001, JDZX2015170, and JDZX2015171), and the National Key Technology R&D Program (Nos. 2013BAI02B01 and 2013BAI13B04). We thank Xue Xu and Lu Zhang for their valuable discussions.

Compliance with ethics guidelines

Kuo Yang, Runshun Zhang, Liyun He, Yubing Li, Wenwen Liu, Changhe Yu, Yanhong Zhang, Xinlong Li, Yan Liu, Weiming Xu, Xuezhong Zhou, and Baoyan Liu declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors.

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2017 Higher Education Press and Springer-Verlag Berlin Heidelberg