Xuezhong Zhou

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.

Data processing and analysis in real‐world traditional Chinese medicine clinical data: challenges and approaches

Traditional Chinese medicine (TCM) is a clinical-based discipline in which real-world clinical practice plays a significant role for both the development of clinical therapy and theoretical research. The large-scale clinical data generated during the daily clinical operations of TCM provide a highly valuable knowledge source for clinical decision making. Secondary analysis of these data would be a vital task for TCM clinical studies before the randomised controlled trials are conducted. In this article, we discuss the challenges and issues, such as structured data curation, data preprocessing and quality, large-scale data management and complex data analysis requirements, in the data processing and analysis of real-world TCM clinical data. Furthermore, we also discuss related state-of-the-art research and solutions in China. We have shown that the clinical data warehouse based on the collection of structured electronic medical record data and clinical terminology would be a promising approach for generating clinical hypotheses and helping the discovery of clinical knowledge from large-scale real-world TCM clinical data.

A novel approach in discovering significant interactions from TCM patient prescription data

The efficacy of a traditional Chinese medicine medication derives from the complex interactions of herbs or Chinese Materia Medica in a formula. The aim of this paper is to propose a new approach to systematically generate combinations of interacting herbs that might lead to good outcome. Our approach was tested on a data set of prescriptions for diabetic patients to verify the effectiveness of detected combinations of herbs. This approach is able to detect effective higher orders of herb-herb interactions with statistical validation. We present an exploratory analysis of clinical records using a pattern mining approach called Interaction Rules Mining.

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.

Topic Model for Chinese Medicine Diagnosis and Prescription Regularities Analysis:Case on Diabetes

Induction of common knowledge or regularities from large-scale clinical data is a vital task for Chinese medicine (CM). In this paper, we propose a data mining method, called the Symptom-Herb-Diagnosis topic (SHDT) model, to automatically extract the common relationships among symptoms, herb combinations and diagnoses from large-scale CM clinical data. The SHDT model is one of the multi-relational extensions of the latent topic model, which can acquire topic structure from discrete corpora (such as document collection) by capturing the semantic relations among words. We applied the SHDT model to discover the common CM diagnosis and treatment knowledge for type 2 diabetes mellitus (T2DM) using 3 238 inpatient cases. We obtained meaningful diagnosis and treatment topics (clusters) from the data, which clinically indicated some important medical groups corresponding to comorbidity diseases (e.g., heart disease and diabetic kidney diseases in T2DM inpatients). The results show that manifestation sub-categories actually exist in T2DM patients that need specific, individualised CM therapies. Furthermore, the results demonstrate that this method is helpful for generating CM clinical guidelines for T2DM based on structured collected clinical data.

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.

A hierarchical symptom-herb topic model for analyzing traditional Chinese medicine clinical diabetic data

Traditional Chinese medicine (TCM) is a clinical medicine. The huge clinical data from the daily clinical process which keeps to TCM theories and principles, is the core empirical knowledge source for TCM research. Induction of the common knowledge or regularities from the large-scale clinical data is a vital task for both theoretical and clinical research of TCM. Topic model have recently shown much success for text analysis and information retrieval by extracting latent topics from text collection. In this paper, we propose a hierarchical symptom-herb topic model (HSHT), to automatically extract the hierarchical latent topic structures with both symptoms and their corresponding herbs in the TCM clinical data. The HSHT model is one of the extensions of hierarchical latent Dirichlet allocation model (hLDA) and Link latent Dirichlet allocation (LinkLDA). The proposed HSHT model is used for extracting the hierarchical structure of symptoms with their corresponding herbs in clinical type 2 diabetes mellitus (T2DM). We get one meaningful super-topic with common symptoms and commonly used herbs and some meaningful subtopics denoted T2DM complications with corresponding symptoms and their commonly used herbs. The results indicate some important medical groups corresponding to the companioned diseases in the T2DM inpatients. And then the results show that there exactly exist TCM diagnosis and treatment sub-categories and the personalized therapies to T2DM. Furthermore, it manifested that the HSHT model is useful for establishing of the TCM clinical guidelines based on the TCM clinical data.

Using link topic model to analyze traditional Chinese Medicine Clinical symptom-herb regularities

Traditional Chinese Medicine (TCM) is a clinical medicine, which focuses on human physiology, pathology, diagnosis and treatment of diseases. Numerous clinical practice and theory research in the TCM field have accumulated huge amount of data. These data include TCM basic databases, TCM literature, as well as a large number of databases or data warehouse on TCM clinical diagnoses and treatment. More and more people pay attention to the discovery of hidden regularities of TCM clinical data. In recent years, topic model has been popularly used for text analysis and information retrieval by extracting latent and significant topics from corpus. In this paper, we apply the Link Latent Dirichlet Allocation (LinkLDA), to automatically extract the latent topic structures which contain the information of both symptoms and their corresponding herbs. By experimental results, the latent topic with symptoms and their corresponding herbs show clinical meaningful results. Furthermore, the model is also compared with other topic models, such as author-topic model, and the result of LinkLDA got better results.

Network Analysis System for Traditional Chinese Medicine Clinical Data

Traditional Chinese medicine (TCM) is a clinical medical discipline with clinical data as one of the main knowledge sources. The clinical information (e.g. symptoms, diagnoses and herb prescriptions) that is captured, generated and used by TCM physicians has complicated inter or intra relationships between the different elements. Due to the co- occurrence and combinational properties, TCM clinical data could naturally be represented by networks. This paper introduces a complex network analysis system to model and analyze the TCM clinical data. The system could automatically generate the networks from the clinical database, query the generated network data from the database and has the social network analysis abilities (e.g. measurements and community identification). It integrated the TCM knowledge (e.g. herb properties) to visualize the clinical data (e.g. herb prescriptions, symptoms and diagnoses) by networks, and can help acquire the core medical structures or relationships from the large-scale clinical data. It shows that the system provides a helpful platform for TCM clinical data analysis and the network analyses could generate clinically meaningful knowledge.