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

Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model

The electronic medical records (EMRs) of traditional Chinese medicine (TCM) include a wealth of TCM knowledge and syndrome diagnosis information, which is crucial for improving the quality of TCM auxiliary decision-making. In practical diagnosis, one disease corresponds to one syndrome, posing considerable hurdles for the informatization of TCM. The purpose of this work was to create an end-to-end TCM diagnostic model, and the knowledge graph (KG) created in this article is used to improve the model's information and realize auxiliary decision-making for TCM disorders. We approached auxiliary decision-making for syndrome differentiation in this article as a multilabel classification task and presented a knowledge-based decision support model for syndrome differentiation (KDSD). Specifically, we created a KG based on TCM features (TCMKG), supplementing the textual representation of medical data with embedded information. Finally, we proposed fusing medical text with KG entity representation (F-MT-KER) to get prediction results using a linear output layer. After obtaining the vector representation of the medical record text using the BERT model, the vector representation of various KG embedded models can provide additional hidden information to a certain extent. Experimental results show that our method improves by 1% (P@1) on the syndrome differentiation auxiliary decision task compared to the baseline model BERT. The usage of EMRs can aid TCM development more efficiently. With the help of entity level representation, character level representation, and model fusion, the multilabel classification method based on the pretraining model and KG can better simulate the TCM syndrome differentiation of the complex cases.

Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021

Background

The practice of traditional Chinese medicine (TCM) began several thousand years ago, and the knowledge of practitioners is recorded in paper and electronic versions of case notes, manuscripts, and books in multiple languages. Developing a method of information extraction (IE) from these sources to generate a cohesive data set would be a great contribution to the medical field. The goal of this study was to perform a systematic review of the status of IE from TCM sources over the last 10 years.

Methods

We conducted a search of four literature databases for articles published from 2010 to 2021 that focused on the use of natural language processing (NLP) methods to extract information from unstructured TCM text data. Two reviewers and one adjudicator contributed to article search, article selection, data extraction, and synthesis processes.

Results

We retrieved 1234 records, 49 of which met our inclusion criteria. We used the articles to (i) assess the key tasks of IE in the TCM domain, (ii) summarize the challenges to extracting information from TCM text data, and (iii) identify effective frameworks, models, and key findings of TCM IE through classification.

Conclusions

Our analysis showed that IE from TCM text data has improved over the past decade. However, the extraction of TCM text still faces some challenges involving the lack of gold standard corpora, nonstandardized expressions, and multiple types of relations. In the future, IE work should be promoted by extracting more existing entities and relations, constructing gold standard data sets, and exploring IE methods based on a small amount of labeled data. Furthermore, fine-grained and interpretable IE technologies are necessary for further exploration.

Patient-Oriented Herb Recommendation System Based on Multi-Graph Convolutional Network

The presented herb recommendation system aims to analyze the patients’ symptoms and recommends a set of herbs as the prescription to treat diseases. In addition to symptoms, the patients’ personal properties and induced diagnoses are also essential for treatment making. Specifically, for different age groups, the treatments are different. However, the existing studies only use symptoms to represent patients and ignore the patients’ multidimensional features modeling. Thus, these models are insufficiently personalized. Meanwhile, most of these existing herb recommendation models based on graphs have not distinguished the effects of different node types. To address the above limitations, we propose a model named Patient-Oriented Multi-Graph Convolutional Network-based Herb Recommendation system (PMGCN). The prediction model contains two effective modules, patient portraits modeling and herb interactions modeling, to learn representations for patients and enhance herb interactions. First, we depict the patient portrait to enrich the individualized features. To distinguish personal properties, symptoms, and diagnoses, we adopt the type-aware attention mechanism, thereby improving the accuracy of personalized herb recommendation. Next, we build two herb-interaction graphs and design type-aware multigraph convolution networks to capture the interactions of herbs and patient features. In this way, our model emphasizes the impact of the patient portrait on diagnosis induction and herb selection. Experimental studies demonstrate that our method outperforms the compared methods and confirms the significance of patient portraits. In conclusion, this research proposes type-aware multigraph convolution networks and adds patient portraits modeling to simulate TCM prescriptions making.

SDTM: A Novel Topic Model Framework for Syndrome Differentiation in Traditional Chinese Medicine

Syndrome differentiation is the most basic diagnostic method in traditional Chinese medicine (TCM). The process of syndrome differentiation is difficult and challenging due to its complexity, diversity, and vagueness. Recently, artificial intelligent methods have been introduced to discover the regularities of syndrome differentiation from TCM medical records, but the existing DM algorithms failed to consider how a syndrome is generated according to TCM theories. In this paper, we propose a novel topic model framework named syndrome differentiation topic model (SDTM) to dynamically characterize the process of syndrome differentiation. The SDTM framework utilizes latent Dirichlet allocation (LDA) to discover the latent semantic relationship between symptoms and syndromes in mass of Chinese medical records. We also use similarity measurement method to make the uninterpretable topics correspond with the labeled syndromes. Finally, Bayesian method is used in the final differentiated syndromes. Experimental results show the superiority of SDTM over existing topic models for the task of syndrome differentiation.

The Impact of Artificial Intelligence on Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

Study on the TCM Syndromes Evolution and Chinese Herbal Characteristics of Type 2 Diabetes Patients with Different Courses of Disease in TCM "Heat Stage": A Real-World Study

OBJECTIVE

The purpose of this study is to analyze and summarize the syndrome distribution, syndrome evolution, and Chinese herb medicine characteristics of T2D in heat stage.

METHOD

In this study, 228 heat-stage T2D patients were divided into three groups based on the course of disease. Group 1 (the course of disease ≤5 years) included 118 patients. Group 2 (5< the course of disease ≤10 years) had 73 patients. Group 3 (the course of disease >10 years) consisted of 37 patients. The main methods used in our study were complex network community partitioning algorithms and Sankey diagram visualization, based on the clinical electronic medical record data we collected.

RESULT

In the three groups, the nodes with the highest node degree are all "heat syndrome." Edge weight between "heat" and "dampness," "qi stagnation," "phlegm," "liver," and "stomach" is the largest. During the whole course of treatment, 60.17%, 63.01%, and 62.16% of the patients' syndromes in groups 1, 2, and 3, respectively, were ascribed to the heat stage all the time. The patients' syndromes in groups 1 and 2 easily transformed to the syndrome of deficiency of both qi and yin of the spleen and stomach. In group 3, 27% of the patients' syndromes were easily transformed into kidney yin deficiency and qi deficiency and blood stasis syndrome. The largest Chinese herb communities of the patients whose syndromes did not change after treatment in the three groups were all heat-clearing drugs. The proportion of blood-activating drugs in patients with syndrome changes increased significantly after treatment.

CONCLUSION

(1) The basic syndrome of T2D patients in the heat stage is liver-stomach heat syndrome. (2) T2D patients in the heat stage tend to deteriorate towards the direction of qi and yin deficiency syndrome. However, the longer the course of the disease is, the more likely it is to deteriorate to the direction of kidney yin deficiency syndrome and blood stasis syndrome. (3) Drugs that can help T2D patients in the heat stage to maintain their condition stably are heat-clearing drugs represented by Coptis chinensis, which usually need to be combined with warming interior drugs such as Zingiberis Rhizoma and Pinelliae Rhizoma.

Efficacy-specific herbal group detection from traditional Chinese medicine prescriptions via hierarchical attentive neural network model

BACKGROUND

Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs.

METHODS

We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage.

RESULTS

We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups.

CONCLUSION

The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

A semi-supervised approach for extracting TCM clinical terms based on feature words

BACKGROUND

A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.

METHODS

The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and leverage extraction results.

RESULTS

Experiment results show that the proposed model improves the extraction of five types of TCM clinical terms, including traditional Chinese medicine, symptoms, patterns, diseases and formulas. The best F1-value of the experiment reaches 78.70% on the test dataset.

CONCLUSIONS

This method can reduce the cost of manual labeling and improve the result in the NER research of TCM clinical terms.

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing

In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell lung cancer (NSCLC) is expensive. To shorten the decision-making process for compounds, we propose a computationally efficient and cost-effective collaborative filtering method with ensemble learning. The ensemble learning is used to handle small-sample sizes in drug response datasets as the typical number of patients in a cancer dataset is very small. Moreover, the proposed method can be used to identify the most suitable compounds for patients without genetic data. To the best of our knowledge, this is the first method to provide effective recommendations without genetic data. We also constructed a reliable dataset that includes eight NSCLC cell lines and ten compounds that have been approved by the Food and Drug Administration. With the new dataset, the experimental results demonstrated that the dataset shift phenomenon that commonly occurs in practical biomedical data does not occur in this problem. The experimental results demonstrated that our proposed method can outperform two state-of-the-art recommender system techniques on both the NCI60 dataset and our new dataset. Our model can be applied to the prediction of drug sensitivity with less labor-intensive experiments in the future.

Use of a tolerance interval approach as a statistical quality control tool for traditional Chinese medicine

Raw materials for traditional Chinese medicine (TCM) are often from different resources and its final product may also be made by different sites. Therefore, variabilities from different resources such as site-to-site or within site component-to-component may be expected. Consequently, test for consistency in raw materials, in-process materials, and/or final product has become an important issue in the quality control (QC) process in TCM development. In this paper, a statistical QC process for raw materials and/or the final product of TCM is proposed based on a two sided [Formula: see text]-content, [Formula: see text]-confidence tolerance interval. More specifically, we construct the tolerance interval for a random-effects model to assess the QC of TCM products from different regions and possibly different product batches. The products can be claimed to be consistency when the constructed tolerance interval is within the permitted range. Given the region and batch effects, sample sizes can also be calculated to ensure the desired measure of goodness. An example is presented to illustrate the proposed approach.

Artificial Intelligence vs. Natural Stupidity: Evaluating AI readiness for the Vietnamese Medical Information System

This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam's healthcare also suffers from the unprepared information infrastructure-using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health, the paper found that the frequency of the word "database" actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as "lacking", "standardizing", "inefficient", and "inaccurate." Finally, manifestations of psycho-cultural elements such as the public's mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector.

Aidi Injection, a Traditional Chinese Medicine Injection, Could Be Used as an Adjuvant Drug to Improve Quality of Life of Cancer Patients Receiving Chemotherapy: A Propensity Score Matching Analysis

Background: Clinical research has paid increasing attention to quality of life (QoL) in recent years, but the assessment of QoL is difficult, hampered by the subjectivity, complexity, and adherence of patients and physicians. According to previous studies, QoL in cancer patients is related to performance status (PS) and influenced by chemotherapy-related toxicity. Aidi injection, a traditional Chinese medicine injection, is used as an adjuvant drug to enhance effectiveness of chemotherapy. The study aims to investigate whether Aidi injection could improve QoL by improving PS and reducing toxicity caused by chemotherapy. Methods: A retrospective cohort study was performed at the First Affiliated Hospital of Anhui Medicine University. Data of consecutive patients diagnosed with cancers between January 2014 and June 2017 were retrieved from the electronic medical record system. After a 1:1 propensity score match, patients were then divided into 2 groups based on the therapies used, that is, Aidi injection combined with chemotherapy and chemotherapy alone, and the PS, chemotherapy-related toxicity, and combined medication information were compared. The effect of different dosages of Aidi injection on patients was further explored. Results: A total of 3200 patients were included in this study. Aidi injection combined with chemotherapy exhibited significantly benefit in PS (P < .001, odds ratio [OR] 3.4, 95% confidence interval [CI] 2.4-4.8) compared with chemotherapy alone after adjusting for the factors that affect PS. The improvement rate of PS in the Aidi group was significantly higher than in the control group across the stratification of gender, age, tumor type, TNM stage, body mass index, nodal metastasis, prior chemotherapy, chemotherapy regimens, other Chinese tradition medicines, and chemotherapy cycle. Meanwhile, Aidi injection used synchronously with chemotherapeutic drugs could decrease the incident rate of damage to liver and kidney function, myelosuppression, and gastrointestinal reactions caused by chemotherapy. Conclusion: It was indicated that the integrative approach combining chemotherapy with Aidi injection, especially with the conventional dosage of Aidi injection, had significant benefit on QoL in cancer patients.

Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model

Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.

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

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.

Development and validation of method for defining conditions using Chinese electronic medical record

Background

The adoption of the electronic medical record (EMR) is rapidly growing in China. Constantly evolving, Chinese EMRs contain vast amounts of clinical and financial data, providing tremendous potential for research and policy use; however, they are only partially standardized and contain free text or unstructured data. To utilize the information contained in Chinese EMRs, the development of data extraction methodology is urgently needed. The purpose of this study is to develop and validate methods to extract clinical information from the Chinese EMR for research use.

Methods

Using 2010 to 2014 EMR data from YouAn Hospital, a large teaching hospital affiliated with Capital Medical University in Beijing, China, we developed extraction methods including 40 EMR definitions for defining 6 liver disease, 5 disease severity conditions, and 29 comorbidities and treatments. We conducted a chart review of 450 randomly selected EMRs. Using physician chart review results as a reference, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to validate each EMR definition.

Results

The sensitivity of the 6 EMR definitions for liver diseases ranged from 78.9 to 100.0 %, and PPV ranged from 82.1 to 100.0 %. The sensitivity of the 5 definitions on disease severity conditions ranged from 91.0 to 100.0 %, and PPV ranged from 79.2 to 100.0 %. Among the 29 EMR definitions for comorbidities and treatments, 23 had sensitivity over 90.0 % and 25 had PPV over 80.0 %. The specificity and NPV for all 40 EMR definitions were over 90.0 %.

Conclusion

The extraction method developed is a valid way of extracting information on liver diseases, comorbidities and related treatments from YouAn hospital EMRs. Our method should be modified for application to other Chinese EMR systems, following our framework for extracting conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0348-6) contains supplementary material, which is available to authorized users.

Data-Driven Information Extraction from Chinese Electronic Medical Records

OBJECTIVE

This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event.

MATERIALS AND METHODS

Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions.

RESULTS

The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846.

DISCUSSION

In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838).

CONCLUSIONS

The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica.

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.

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.

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.

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.

Promoting integrative medicine by computerization of traditional Chinese medicine for scientific research and clinical practice: The SuiteTCM Project

BACKGROUND

Chinese and contemporary Western medical practices evolved on different cultures and historical contexts and, therefore, their medical knowledge represents this cultural divergence. Computerization of traditional Chinese medicine (TCM) is being used to promote the integrative medicine to manage, process and integrate the knowledge related to TCM anatomy, physiology, semiology, pathophysiology, and therapy.

METHODS

We proposed the development of the SuiteTCM software, a collection of integrated computational models mainly derived from epidemiology and statistical sciences for computerization of Chinese medicine scientific research and clinical practice in all levels of prevention. The software includes components for data management (DataTCM), simulation of cases (SimTCM), analyses and validation of datasets (SciTCM), clinical examination and pattern differentiation (DiagTCM, TongueTCM, and PulseTCM), intervention selection (AcuTCM, HerbsTCM, and DietTCM), management of medical records (ProntTCM), epidemiologic investigation of sampled data (ResearchTCM), and medical education, training, and assessment (StudentTCM).

DISCUSSION

The SuiteTCM project is expected to contribute to the ongoing development of integrative medicine and the applicability of TCM in worldwide scientific research and health care. The SuiteTCM 1.0 runs on Windows XP or later and is freely available for download as an executable application.