Understanding traditional Chinese medicine via statistical learning of expert-specific Electronic Medical Records

Abstract

Background: Traditional Chinese medicine (TCM) has been attracting lots of attentions from various disciplines recently. However, TCM is still mysterious because of its unique philosophy and theoretical thinking. Due to the lack of high quality data, understanding TCM thoroughly faces critical challenges. In this study, we introduce the Zhou Archive, a large-scale database of expert-specific Electronic Medical Records containing information about 73,000+ visits to one TCM doctor for over 35 years. Covering the full spectrum of diagnosis-treatment model behind TCM practice, the archive provides an opportunity to understand TCM from the data-driven perspective.

Methods: Processing the text data in the archive via a series of data processing steps, we transformed the semi-structured EMRs in the archive to a well-structured feature table. Based on the structured feature table obtained, a series of statistical analyses are implemented to learn principles of TCM clinical practice from the archive, including correlation analysis, enrichment analysis, embedding analysis and association pattern discovery.

Results: A structured feature table of 14,000+ features is generated at the end of the proposed data processing procedure, with a feature codebook, a term dictionary and a term-feature map as byproducts. Statistical analysis of the feature table reveals underlying principles about the diagnosis-treatment model of TCM, helping us better understand the TDM practice from a data-driven perspective.

Conclusion: Expert-specific EMRs provide opportunities to understand TCM from the data-driven perspective. Taking advantage of recent progresses on NLP for Chinese, we can process a large number of TCM EMRs efficiently to gain insights via statistical analysis.

Graphical abstract

Keywords

TCM / EMRs / data-driven perspective / Chinese text mining / statistical analysis

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.1007/s40484-019-0173-x.

ACKNOWLEDGEMENT

We thank the Zhou Zhongying’s Studio at Nanjing University of Chinese Medicine for the great efforts on collecting, managing and sharing this valuable archive. We also thank Miss Bing Liang, Mr. Qiuyu Liang and Miss Che Wang for their efforts on data preparation and preprocessing. ƒThis work was partially supported by the National Natural Science Foundation of China (Nos. 11771242 & 11401338), the Tsinghua University Initiative Scientific Research Program and Supporting Grant to the Zhou Zhongying’s Studio 201159 by the State Administration of TCM of China.

COMPLIANCE WITH ETHICS GUDELINES

The authors Yang Yang, Qi Li, Zhaoyang Liu, Fang Ye and Ke Deng declare that they have no conflict of interests.ƒAll procedures were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

RIGHTS & PERMISSIONS

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