Traditional Chinese Medicine Data Research Articles

ZHENG May Contribute to Obesity Phenotypes Based on Body Composition: A Pilot Study on the Traditional Chinese Medicine Approach

Objective. Obesity therapy needs new approaches to complement current phenotyping systems. This study aims to assess associations between the Traditional Chinese Medicine (TCM) ZHENG and obesity phenotypes. Methods. We assessed medical history and habitual physical activity and measured body composition, fasting plasma glucose and insulin, and lipids. We collected TCM data through face-to-face interview. ZHENG elements (essentials and locations) were identified by TCM practitioner. Primary ZHENG was assessed by cluster analysis. Results. In 140 consecutive subjects enrolled in a university clinic (body mass index (BMI): 39.9 ± 5.8 kg/m2), ZHENG essentials were identified as “QiXu,” “Re,” “YinXu,” and “TanShi” (totally 86.8%). Locations were “Shen,” “Wei,” “Pi,” and “Gan” (totally 91.8%). Four types of primary ZHENG were identified: A (37.1% of subjects), B (16.5%), C (35.7%), and D (10.7%). Subjects in type D showed elevated BMI, total fat mass (FM), FM index, trunk FM, and less physical activity, as compared with others. Subjects in type B changed regional body composition (reduced trunk FM% and elevated appendicular FM%). Biological parameters did not differ across primary ZHENG clusters. Conclusions. Obesity phenotypes based on body composition differ according to ZHENG in obese patients. This study is a first step toward understanding the contribution of TCM to obesity phenotyping.

Modern bioinformatics meets traditional Chinese medicine

Traditional Chinese medicine (TCM) is gaining increasing attention with the emergence of integrative medicine and personalized medicine, characterized by pattern differentiation on individual variance and treatments based on natural herbal synergism. Investigating the effectiveness and safety of the potential mechanisms of TCM and the combination principles of drug therapies will bridge the cultural gap with Western medicine and improve the development of integrative medicine. Dealing with rapidly growing amounts of biomedical data and their heterogeneous nature are two important tasks among modern biomedical communities. Bioinformatics, as an emerging interdisciplinary field of computer science and biology, has become a useful tool for easing the data deluge pressure by automating the computation processes with informatics methods. Using these methods to retrieve, store and analyze the biomedical data can effectively reveal the associated knowledge hidden in the data, and thus promote the discovery of integrated information. Recently, these techniques of bioinformatics have been used for facilitating the interactional effects of both Western medicine and TCM. The analysis of TCM data using computational technologies provides biological evidence for the basic understanding of TCM mechanisms, safety and efficacy of TCM treatments. At the same time, the carrier and targets associated with TCM remedies can inspire the rethinking of modern drug development. This review summarizes the significant achievements of applying bioinformatics techniques to many aspects of the research in TCM, such as analysis of TCM-related '-omics' data and techniques for analyzing biological processes and pharmaceutical mechanisms of TCM, which have shown certain potential of bringing new thoughts to both sides.

Traditional chinese medicine zheng in the era of evidence-based medicine: a literature analysis.

Zheng, which is also called a syndrome or pattern, is the basic unit and a key concept of traditional Chinese medicine (TCM) theory. Zheng can be considered a further stratification of patients when it is integrated with biomedical diagnoses in clinical practice to achieve higher efficacies. In an era of evidence-based medicine, confronted with the vast and increasing volume of TCM data, there is an urgent need to explore these resources effectively using techniques of knowledge discovery in databases. The application of effective data mining in the analysis of multiple extensively integrated databases can supply new information about TCM Zheng research. In this paper, we screened the published literature on TCM Zheng-related studies in the SinoMed and PubMed databases with a novel data mining approach to obtain an overview of the Zheng research landscape in the hope of contributing to a better understanding of TCM Zheng in the era of evidence-based medicine. In our results, contrast was found in Zheng in different studies, and several determinants of Zheng were identified. The data described in this paper can be used to assess Zheng research studies based on the title and certain characteristics of the abstract. These findings will benefit modern TCM Zheng-related studies and guide future Zheng study efforts.

Retrospective collection, arrangement and application of traditional Chinese medicine statistical data

The data on Traditional Chinese Medicine (TCM) has not been fully exploited,resulting in the gap between the storage and usage of the TCM data resources. The study on the retrospective collection, arrangement and application of Traditional Chinese Medicine Statistical Data aims to enhance the usage of TCM data, basing on the collection and arrangement of the authoritative TCM data, common analysis of the TCM data from the founding of the People Republic of China.A pilot TCM Statistics Information Database and a TCM Health Statistics Data Query System have been established with the computer database technology, the object-oriented programming tool (C#) and the Internet Browser-based presentation programming tool (ASP. Net), providing the initial functions of the data retrieval and the graphical presentation of the search results on TCM statistics data. This paper explores statistical data-oriented with attributes description as the idea to establish the database of complex data, with the solution of the data problems such as numerous of data, diverse of data units, lack of regularity and hard to be classified. The study realizes the building of TCM Statistics Information Database and the retrieval finction of TCM data, which lays a foundation on the building of Traditional Chinese Medicine Industry Statistics Database and the information services of TCM statistics data in the future. Key words: Health Statistics; Database; Data Arrangement; Programming Technology; Application Research; Traditional Chinese Medicine (TCM)

Chemoinformatics Approaches for Traditional Chinese Medicine Research and Case Application in Anticancer Drug Discovery

Traditional Chinese Medicine (TCM), which has been used for thousands of years to treat diseases, provides unique theoretical and practical methodologies for disease control. With the increasing accumulation of TCM data, it is imperative to study and analyze these resources with modern technologies and to elucidate the molecular mechanisms of TCM therapy. However, the philosophy, framework and technique of TCM are quite different from those of Western medicine, which causes complications when attempting to design modern drug treatments based on TCM. To meet this challenge, some basic chemoinformatics techniques, including molecular similarity searching, virtual screening and inverse docking, have been utilized in an attempt to gain a deeper understanding of TCM and to accelerate the TCM-based drug discovery. Recent progress on the use of chemoinformatics in TCM research will be discussed and an example of the preliminary application of chemoinformatics methods in anticancer drug design will be provided.

Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support

Traditional Chinese medicine (TCM) is a scientific discipline, which develops the related theories from the long-term clinical practices. The large-scale clinical data are the core empirical knowledge source for TCM research. This paper introduces a clinical data warehouse (CDW) system, which incorporates the structured electronic medical record (SEMR) data for medical knowledge discovery and TCM clinical decision support (CDS). We have developed the clinical reference information model (RIM) and physical data model to manage the various information entities and their relationships in TCM clinical data. An extraction-transformation-loading (ETL) tool is implemented to integrate and normalize the clinical data from different operational data sources. The CDW includes online analytical processing (OLAP) and complex network analysis (CNA) components to explore the various clinical relationships. Furthermore, the data mining and CNA methods are used to discover the valuable clinical knowledge from the data. The CDW has integrated 20,000 TCM inpatient data and 20,000 outpatient data, which contains manifestations (e.g. symptoms, physical examinations and laboratory test results), diagnoses and prescriptions as the main information components. We propose a practical solution to accomplish the large-scale clinical data integration and preprocessing tasks. Meanwhile, we have developed over 400 OLAP reports to enable the multidimensional analysis of clinical data and the case-based CDS. We have successfully conducted several interesting data mining applications. Particularly, we use various classification methods, namely support vector machine, decision tree and Bayesian network, to discover the knowledge of syndrome differentiation. Furthermore, we have applied association rule and CNA to extract the useful acupuncture point and herb combination patterns from the clinical prescriptions. A CDW system consisting of TCM clinical RIM, ETL, OLAP and data mining as the core components has been developed to facilitate the tasks of TCM knowledge discovery and CDS. We have conducted several OLAP and data mining tasks to explore the empirical knowledge from the TCM clinical data. The CDW platform would be a promising infrastructure to make full use of the TCM clinical data for scientific hypothesis generation, and promote the development of TCM from individualized empirical knowledge to large-scale evidence-based medicine.

Automatic symptom name normalization in clinical records of traditional Chinese medicine

BackgroundIn recent years, Data Mining technology has been applied more than ever before in the field of traditional Chinese medicine (TCM) to discover regularities from the experience accumulated in the past thousands of years in China. Electronic medical records (or clinical records) of TCM, containing larger amount of information than well-structured data of prescriptions extracted manually from TCM literature such as information related to medical treatment process, could be an important source for discovering valuable regularities of TCM. However, they are collected by TCM doctors on a day to day basis without the support of authoritative editorial board, and owing to different experience and background of TCM doctors, the same concept might be described in several different terms. Therefore, clinical records of TCM cannot be used directly to Data Mining and Knowledge Discovery. This paper focuses its attention on the phenomena of "one symptom with different names" and investigates a series of metrics for automatically normalizing symptom names in clinical records of TCM.ResultsA series of extensive experiments were performed to validate the metrics proposed, and they have shown that the hybrid similarity metrics integrating literal similarity and remedy-based similarity are more accurate than the others which are based on literal similarity or remedy-based similarity alone, and the highest F-Measure (65.62%) of all the metrics is achieved by hybrid similarity metric VSM+TFIDF+SWD.ConclusionsAutomatic symptom name normalization is an essential task for discovering knowledge from clinical data of TCM. The problem is introduced for the first time by this paper. The results have verified that the investigated metrics are reasonable and accurate, and the hybrid similarity metrics are much better than the metrics based on literal similarity or remedy-based similarity alone.

Research on Data Quality of Chinese Medicine Scientific Data

Abstract Based on the achievement of Ministry of Science and Technology (MOST) fundamental research project of traditional Chinese medicine (TCM) scientific data management and shared services center (2004DKA20250), and taken TCM 110 databases, totally 260G data as basic data source, this article mainly discusses the construction of TCM data warehouse and data integration. Starting with the Meta data model, we analyzed the assessment standards and key dimensions evaluation algorithm of TCM data quality, and then we will accomplish a high data quality of TCM data warehouse.

KISTCM: knowledge discovery system for traditional Chinese medicine

Objective: Traditional Chinese Medicine (TCM) provides an alternative method for achieving and maintaining good health. Due to the increasing prevalence of TCM and the large volume of TCM data accumulated though thousands of years, there is an urgent need to efficiently and effectively explore this information and its hidden rules with knowledge discovery in database (KDD) techniques. This paper describes the design and development of a knowledge discovery system for TCM as well as the newly proposed KDD techniques integrated in this system. Methods: A novel Knowledge dIscovery System for TCM (KISTCM) is developed by incorporating several data mining techniques, primarily including a medicine dependency relationship discovery algorithm, an efficacy dimension reduction algorithm based on neural networks, a method for exploring the relationships between formulae and syndromes using gene expression programming (GEP), and an approach for discovering the properties in terms of nature, taste and meridian based on the herbal dosage by employing the effect degree function to calculate the effect of each property. Results: Representative experimental cases are used to evaluate the system performance. Encouraging results are obtained, including rules previously unknown to algorithm designers and experiment runners. Experiments demonstrate that KISTCM has powerful knowledge discovery and data analysis capabilities, and is a useful tool for discovering the underlying rules in formulae. Our proposed techniques successfully discover hidden knowledge from TCM data, which is a new direction in knowledge discovery. From TCM experts' perspective, the accuracy of data analysis for KISTCM is an improvement, and these results compare favorably to other existing TCM data mining techniques. The system could be expected to be useful in the practice of TCM, e.g., assisting TCM physicians in prescribing formulae or automatically distinguishing between minister and assistant herbs in a formula.

Statistical Validation of Traditional Chinese Medicine Theories

The theories of Traditional Chinese Medicine (TCM) originated from experiences doctors had with patients in ancient times. We ask the question whether aspects of TCM theories can be reconstructed through data analysis. To answer the question, we have developed a data analysis method called latent tree models and have used it to analyze several TCM data sets. This paper reports the results we obtained on one of the data sets and explains how they provide statistical validation to the relevant TCM theories.

The need for traditional Chinese medicine morbidity research

Traditional Chinese medicine (TCM) has long justified its practice on empirical phenomenology. Questions remain, however, as to the legitimacy of this approach and its relevance to TCM practice and educational developments in the 21st century. General medical practice (GMP) has used practice-based data collected over the past 25 years to develop relevant medical curriculum and enhance patient care. As TCM becomes ever more incorporated into Western educational and health systems, it too must concern itself with providing a solid evidence base to be used in enhancing patient care and educational curriculum. Computer generated data related to patient problems or problem complexes abstracted from TCM clinical records can provide the basis for practitioner development and the enhancement of training programs that will lead to improvements in patient care. Such locally valid and representative data can also be used to inform research programs, public policy and evidence-based commissioning. The key to developing such systems is standardising TCM terminology and data collection protocols.