Yinghui Wang

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

OBJECTIVE 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). MATERIALS AND METHODS 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. RESULTS 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. CONCLUSION 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.

Novel two-stage analytic approach in extraction of strong herb-herb interactions in TCM clinical treatment of insomnia

In this paper, we aim to investigate strong herb-herb interactions in TCM for effective treatment of insomnia. Given that extraction of herb interactions is quite similar to gene epistasis study due to non-linear interactions among their study factors, we propose to apply Multifactor Dimensionality Reduction (MDR) that has shown useful in discovering hidden interaction patterns in biomedical domains. However, MDR suffers from high computational overhead incurred in its exhaustive enumeration of factors combinations in its processing. To address this drawback, we introduce a two-stage analytical approach which first uses hierarchical core sub-network analysis to pre-select the subset of herbs that have high probability in participating in herb-herb interactions, which is followed by applying MDR to detect strong attribute interactions in the pre-selected subset. Experimental evaluation confirms that this approach is able to detect effective high order herb-herb interaction models in high dimensional TCM insomnia dataset that also has high predictive accuracies.

Building Clinical Data Warehouse for Traditional Chinese Medicine Knowledge Discovery

The clinical data from the daily clinical process, which keeps to traditional Chinese medicine (TCM) theories and principles, is the core empirical knowledge source for TCM researches. This paper introduces a data warehouse system, which is based on the structured electronic medical record system and daily clinical data, for TCM clinical researches and medical knowledge discovery. The system consists of several key components: clinical data schema, extraction-transformation-loading tool, online analytical analysis (OLAP) based on Business Objects (a commercial business intelligence software), and integrated data mining functionalities. Currently, the data warehouse contains 20,000 inpatient data of diabetes, coronary heart disease and stroke, and more than 20,000 outpatient data. Moreover, we have developed several important research oriented subject analyses using OLAP, and conducted several TCM clinical data mining applications. The analysis applications show that the developed clinical data warehouse platform is promising to build the bridge for TCM clinical practice and theoretical research, hence, will promote the related TCM researches.

Patterns of herbal combination for the treatment of insomnia commonly employed by highly experienced Chinese medicine physicians

ObjectiveTo explore the most effective herbal combinations commonly used by highly experienced Chinese medicine (CM) physicians for the treatment of insomnia.MethodsWe collected and analyzed data related to insomnia treatment from the clinics of 7 highly experienced CM physicians in Beijing. The sample included 162 patients and 460 consultations in total. Patient outcomes, such as sleep quality and sleep time per day, were manually collected from the medical records by trained CM clinicians. Three data mining methods, support vector machine (SVM), logistic regression and decision tree, and multifactor dimensionality reduction (MDR), were used to determine and confirm the herbal combinations that resulted in positive outcomes in patients suffering from insomnia.ResultsResults show that MDR is the most efficient method to predict the effective herbal combinations. Using the MDR model, we identified several combinations of herbs with 100% positive outcomes, such as stir-fried spine date seed, Szechwan lovage rhizome, and prepared thinleaf milkwort root; white peony root, golden thread, and stir-fried spine date seed; and Asiatic cornelian cherry fruit with fresh rehmannia.ConclusionsResults indicate that herbal combinations are effective treatments for patients with insomnia compared with individual herbs. It is also shown that MDR is a potent data mining method to identify the herbal combination with high rates of positive outcome.

Detection of Herb-Symptom Associations from Traditional Chinese Medicine Clinical Data

Background. Traditional Chinese medicine (TCM) is an individualized medicine by observing the symptoms and signs (symptoms in brief) of patients. We aim to extract the meaningful herb-symptom relationships from large scale TCM clinical data. Methods. To investigate the correlations between symptoms and herbs held for patients, we use four clinical data sets collected from TCM outpatient clinical settings and calculate the similarities between patient pairs in terms of the herb constituents of their prescriptions and their manifesting symptoms by cosine measure. To address the large-scale multiple testing problems for the detection of herb-symptom associations and the dependence between herbs involving similar efficacies, we propose a network-based correlation analysis (NetCorrA) method to detect the herb-symptom associations. Results. The results show that there are strong positive correlations between symptom similarity and herb similarity, which indicates that herb-symptom correspondence is a clinical principle adhered to by most TCM physicians. Furthermore, the NetCorrA method obtains meaningful herb-symptom associations and performs better than the chi-square correlation method by filtering the false positive associations. Conclusions. Symptoms play significant roles for the prescriptions of herb treatment. The herb-symptom correspondence principle indicates that clinical phenotypic targets (i.e., symptoms) of herbs exist and would be valuable for further investigations.

Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine

Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.

A MDP solution for Traditional Chinese medicine treatment planning

Herbal medicine is the primary method of treatment in Traditional Chinese medicine (TCM) which proposes an essential health solution in China. Medical treatments are usually made by TCM physicians sequentially in an uncertain environment. Markov Decision Process (MDP) provides a powerful mathematical technique for planning in environment under uncertainty and is suitable for TCM therapy planning. In this paper, we apply MDP to solve TCM herbal treatment planning with all the parameters inferred from TCM clinical data for patient with type 2 diabetes. This MDP model contains 30 health states obtained using k-means clustering algorithm and 159 actions of basic prescriptions. This model could order sequences of prescriptions from the action set for patients with type 2 diabetes. The results show that the MDP model for TCM treatment planning can identify and order useful prescriptions which are reasonable in clinical practice.

Clinical data quality problems and countermeasure for real world study

Real world study (RWS) has become a hotspot for clinical research. Data quality plays a vital role in research achievement and other clinical research fields. In this paper, the common quality problems in the RWS of traditional Chinese medicine are discussed, and a countermeasure is proposed.

Data Mining in Real-World Traditional Chinese Medicine Clinical Data Warehouse

Real-world clinical setting is the major arena of traditional Chinese medicine (TCM) as it has experienced long-term practical clinical activities, and developed established theoretical knowledge and clinical solutions suitable for personalized treatment. Clinical phenotypes have been the most important features captured by TCM for diagnoses and treatment, which are diverse and dynamically changeable in real-world clinical settings. Together with clinical prescription with multiple herbal ingredients for treatment, TCM clinical activities embody immense valuable data with high dimensionalities for knowledge distilling and hypothesis generation. In China, with the curation of large-scale real-world clinical data from regular clinical activities, transforming the data to clinical insightful knowledge has increasingly been a hot topic in TCM field. This chapter introduces the application of data warehouse techniques and data mining approaches for utilizing real-world TCM clinical data, which is mainly from electronic medical records. The main framework of clinical data mining applications in TCM field is also introduced with emphasizing on related work in this field. The key points and issues to improve the research quality are discussed and future directions are proposed.

Experience inheritance from famous specialists based on real-world clinical research paradigm of traditional Chinese medicine

The current modes of experience inheritance from famous specialists in traditional Chinese medicine (TCM) include master and disciple, literature review, clinical-epidemiology-based clinical research observation, and analysis and data mining via computer and database technologies. Each mode has its advantages and disadvantages. However, a scientific and instructive experience inheritance mode has not been developed. The advent of the big data era as well as the formation and practice accumulation of the TCM clinical research paradigm in the real world have provided new perspectives, techniques, and methods for inheriting experience from famous TCM specialists. Through continuous exploration and practice, the research group proposes the innovation research mode based on the real-world TCM clinical research paradigm, which involves the inheritance and innovation of the existing modes. This mode is formulated in line with its own development regularity of TCM and is expected to become the main mode of experience inheritance in the clinical field.