Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes.

This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end-to-end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream.

Question Answering System Based on Knowledge Graph in Traditional Chinese Medicine Diagnosis and Treatment of Viral Hepatitis B.

This article uses the real medical records and web pages of Chinese medicine diagnosis and treatment of hepatitis B to extract structured medical knowledge, and obtains a total of 8,563 entities, 96,896 relationships, 32 entity types, and 40 relationship types. The structured data was stored in the Neo4j graph structure database, and a knowledge graph of Chinese medical diagnosis and treatment of hepatitis B was constructed. The knowledge map is used as a structured data source to provide high-quality knowledge information for the medical question and answer system based on hepatitis B disease. Applying the deep learning method to the question identification and knowledge response of the question answering system makes the hepatitis B medical intelligent question answering system has important research and application significance. The question-and-answer system takes aim at hepatitis B, a public health problem in the world and leverages the advantages of traditional Chinese medicine for diagnosis and treatment. It provides a reference for doctors' disease diagnosis, treatment, and patient self-care. Its value is important for the treatment of hepatitis B disease.

End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation.

BACKGROUND: Traditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients. OBJECTIVE: The objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets. METHODS: We approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning-based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models. RESULTS: The F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding-based RCNN was 10% higher than that of the character encoding-based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models. CONCLUSIONS: Medical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning-based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer.

Analyzing hedyotis diffusa mechanisms of action from the genomics perspective.

BACKGROUND AND OBJECTIVE: Hedyotis diffusa is an herb used for anti-cancer, anti-oxidant, anti-inflammatory, and anti-fibroblast treatment in the clinical practice of Traditional Chinese Medicine. However, its pharmacological mechanisms have not been fully established and there is a lack of modern scientific verification. One of the best ways to further understand Hedyotis diffusa's mechanisms of action is to analyze it from the genomics perspective. METHODS: In this study, we used network pharmacology approaches to infer the herb-gene interactions, the herb-pathway interactions, and the gene families. We then analyzed Hedyotis diffusa's mechanisms of action using the genomics context combined with the Traditional Chinese Medicine clinical practice and the pharmacological research. RESULTS: The results obtained in the pathway and gene family analysis were consistent with the Traditional Chinese Medicine clinical experience and the pharmacological activities of Hedyotis diffusa. CONCLUSIONS: Our approach can identify related genes and pathways correctly with little a priori knowledge, and provide potential directions to facilitate further research.

Xueshuantong Injection (Lyophilized) Attenuates Cerebral Ischemia/Reperfusion Injury by the Activation of Nrf2-VEGF Pathway.

Xueshuantong injection (Lyophilized, XST), extracted from the traditional Chinese medicinal herb Panax notoginseng, has neuroprotective effect on cerebral ischemia. Revascularization of ischemic tissue is good for the therapy of cerebrovascular disease. In this study, angiogenic potentiality and possible mechanism of XST for cerebral ischemia were explored. Rats were subjected to transient middle cerebral artery occlusion (MCAO), and then intraperitoneally administered with XST daily for 3 or 7 consecutive days. The neurological function deficits, and endogenous antioxidant capacity were evaluated. Post-stroke angiogenesis and vascularization were assessed by ELISA and immunohistochemical staining. Transcription levels of Nrf2, HO-1, NQO1 in brain tissues were analyzed by real-time RT-PCR. The results showed that XST could remarkably ameliorate neuronal functional deficit, promote angiogenesis and vascularization after MCAO. The mechanism of angiogenesis might be related to endogenous antioxidant capacity and Nrf2 pathway. In conclusion, administered XST for 7 days after stroke could significantly improve functional recovery and promote angiogenesis, that might be related to Nrf2 signaling pathway. These findings could provide scientific evidence for the use of XST in cerebral ischemic diseases and provide theoretical support for further studies.

Cluster analysis for syndromes of real-world coronary heart disease with angina pectoris.

Syndromes of coronary heart disease with angina pectoris were analyzed to provide guidance for clinical practice and to improve accuracy of traditional Chinese medicine (TCM) diagnoses and efficacy of TCM treatment. A total of 860 cases with coronary heart disease with angina pectoris were selected from TCM Clinical Research Information Sharing System for TCM clinics and research. Syndromes were automatically extracted with the cluster method and were analyzed to provide objective evidence for clinical studies. Final syndrome classifications were recognized and confirmed by clinical experts. Popular syndromes included Qi and blood deficiency, blood stasis and obstruction collaterals, liver depression and spleen deficiency, and Qi stagnation and blood stasis. Syndromes Qi and blood deficiency and blood stasis and obstruction collaterals accounted for 28.61% of total syndromes, whereas liver depression and spleen deficiency and Qi stagnation and blood stasis accounted for 26.44%. The main syndrome elements comprised Qi deficiency, blood deficiency, blood stasis, and Qi stagnation.

[Effects of transcutaneous electrical acupoint stimulation on treatment and prevention of postoperative sore throat after tracheal intubation under general anesthesia].

OBJECTIVE: To observe the clinical efficacy of transcutaneous electrical acupoint stimulation (TEAS) at Hegu (LI 4) and Neiguan (PC 6) on treatment and prevention of postoperative sore throat (POST) after tracheal intubation under general anesthesia. METHODS: One hundred patients who received elective thyroid gland lobectomy with gradeⅠand Ⅱ of American Society of Anesthesiologists criteria were randomly assigned into a TEAS group and an anesthesia group according to random number table method, 50 cases in each group. All the patients were treated with tracheal intubation under general anesthesia. Patients in the TEAS group were treated with TEAS (2 Hz/100 Hz, 8 to 12 mA) at Hegu (LI 4) and Neiguan (PC 6) from 30 min before anesthesia induction to the end of operation. Patients in the anesthesia group were treated with TEAS at the same acupoints but no electrical stimulation was given. The incidence rate, severity and visual analogue scale (VAS) of POST were recorded 1h, 6h, 12h and 24h after tracheal extubation, respectively. RESULTS: The incidence rate of POST was 12.0% (6/50), 22.0% (11/50) and 18.0% (9/50) 1h, 6h, 12h after tracheal extubation respectively in the TEAS group, which was significantly lower than 30.0% (15/50), 42.0% (21/50) and 36.0% (18/50) in the anesthesia group (all P<0.05). However, the incidence rate was not significantly different between the two groups 24h after extubation[14.0% (7/50) vs 28.0% (14/50), P>0.05]. Moreover, the VAS scores of the patients with POST in the TEAS group at each time point were lower than those in the anesthesia group (all P<0.05). CONCLUSIONS: TEAS at Hegu (LI 4) and Neiguan (PC 6) can effectively reduce the incidence rate and severity of POST induced by tracheal intubation under general anesthesia.

Panax ginseng genome examination for ginsenoside biosynthesis.

Ginseng, which contains ginsenosides as bioactive compounds, has been regarded as an important traditional medicine for several millennia. However, the genetic background of ginseng remains poorly understood, partly because of the plant's large and complex genome composition. We report the entire genome sequence of Panax ginseng using next-generation sequencing. The 3.5-Gb nucleotide sequence contains more than 60% repeats and encodes 42 006 predicted genes. Twenty-two transcriptome datasets and mass spectrometry images of ginseng roots were adopted to precisely quantify the functional genes. Thirty-one genes were identified to be involved in the mevalonic acid pathway. Eight of these genes were annotated as 3-hydroxy-3-methylglutaryl-CoA reductases, which displayed diverse structures and expression characteristics. A total of 225 UDP-glycosyltransferases (UGTs) were identified, and these UGTs accounted for one of the largest gene families of ginseng. Tandem repeats contributed to the duplication and divergence of UGTs. Molecular modeling of UGTs in the 71st, 74th, and 94th families revealed a regiospecific conserved motif located at the N-terminus. Molecular docking predicted that this motif captures ginsenoside precursors. The ginseng genome represents a valuable resource for understanding and improving the breeding, cultivation, and synthesis biology of this key herb.

A novel classification method for aid decision of traditional Chinese patent medicines for stroke treatment.

Traditional Chinese patent medicines are widely used to treat stroke because it has good efficacy in the clinical environment. However, because of the lack of knowledge on traditional Chinese patent medicines, many Western physicians, who are accountable for the majority of clinical prescriptions for such medicine, are confused with the use of traditional Chinese patent medicines. Therefore, the aid-decision method is critical and necessary to help Western physicians rationally use traditional Chinese patent medicines. In this paper, Manifold Ranking is employed to develop the aid-decision model of traditional Chinese patent medicines for stroke treatment. First, 115 stroke patients from three hospitals are recruited in the cross-sectional survey. Simultaneously, traditional Chinese physicians determine the traditional Chinese patent medicines appropriate for each patient. Second, particular indicators are explored to characterize the population feature of traditional Chinese patent medicines for stroke treatment. Moreover, these particular indicators can be easily obtained byWestern physicians and are feasible for widespread clinical application in the future. Third, the aid-decision model of traditional Chinese patent medicines for stroke treatment is constructed based on Manifold Ranking. Experimental results reveal that traditional Chinese patent medicines can be differentiated. Moreover, the proposed model can obtain high accuracy of aid decision.

A novel multi-target regression framework for time-series prediction of drug efficacy.

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

Classification of facial diagnosis gloss in Chinese medicine based on different algorithms.

OBJECTIVE: To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images. METHODS: To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and Lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)2PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method. RESULTS: All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the Lab was 7%-10% higher than that of PCA, 2DPCA, (2D)2PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the Lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%. CONCLUSION: A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.

Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine.

Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differentiation. We adopt statistics method to describe the syndrome factor regular pattern of spaceman volunteers. Hybrid optimization based multilabel (HOML) is used as feature selection method and multilabel k-nearest neighbors (ML-KNN) is applied. According to the syndrome factor statistical result, we find that qi deficiency is a base syndrome pattern throughout the entire experiment process and, at the same time, there are different associated syndromes such as liver depression, spleen deficiency, dampness stagnancy, and yin deficiency, due to differences of individual situation. With feature selection, we screen out ten key factors which are essential to syndrome differentiation in TCM. The average precision of multilabel classification model reaches 80%.

ISMAC: An Intelligent System for Customized Clinical Case Management and Analysis.

Clinical cases are primary and vital evidence for Traditional Chinese Medicine (TCM) clinical research. A great deal of medical knowledge is hidden in the clinical cases of the highly experienced TCM practitioner. With a deep Chinese culture background and years of clinical experience, an experienced TCM specialist usually has his or her unique clinical pattern and diagnosis idea. Preserving huge clinical cases of experienced TCM practitioners as well as exploring the inherent knowledge is then an important but arduous task. The novel system ISMAC (Intelligent System for Management and Analysis of Clinical Cases in TCM) is designed and implemented for customized management and intelligent analysis of TCM clinical data. Customized templates with standard and expert-standard symptoms, diseases, syndromes, and Chinese Medince Formula (CMF) are constructed in ISMAC, according to the clinical diagnosis and treatment characteristic of each TCM specialist. With these templates, clinical cases are archived in order to maintain their original characteristics. Varying data analysis and mining methods, grouped as Basic Analysis, Association Rule, Feature Reduction, Cluster, Pattern Classification, and Pattern Prediction, are implemented in the system. With a flexible dataset retrieval mechanism, ISMAC is a powerful and convenient system for clinical case analysis and clinical knowledge discovery.

Patient classification of hypertension in Traditional Chinese Medicine using multi-label learning techniques.

BACKGROUND: Hypertension is one of the major risk factors for cardiovascular diseases. Research on the patient classification of hypertension has become an important topic because Traditional Chinese Medicine lies primarily in "treatment based on syndromes differentiation of the patients". METHODS: Clinical data of hypertension was collected with 12 syndromes and 129 symptoms including inspection, tongue, inquiry, and palpation symptoms. Syndromes differentiation was modeled as a patient classification problem in the field of data mining, and a new multi-label learning model BrSmoteSvm was built dealing with the class-imbalanced of the dataset. RESULTS: The experiments showed that the BrSmoteSvm had a better results comparing to other multi-label classifiers in the evaluation criteria of Average precision, Coverage, One-error, Ranking loss. CONCLUSIONS: BrSmoteSvm can model the hypertension's syndromes differentiation better considering the imbalanced problem.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

A Novel Classification Method for Syndrome Differentiation of Patients with AIDS.

We consider the analysis of an AIDS dataset where each patient is characterized by a list of symptoms and is labeled with one or more TCM syndromes. The task is to build a classifier that maps symptoms to TCM syndromes. We use the minimum reference set-based multiple instance learning (MRS-MIL) method. The method identifies a list of representative symptoms for each syndrome and builds a Gaussian mixture model based on them. The models for all syndromes are then used for classification via Bayes rule. By relying on a subset of key symptoms for classification, MRS-MIL can produce reliable and high quality classification rules even on datasets with small sample size. On the AIDS dataset, it achieves average precision and recall 0.7736 and 0.7111, respectively. Those are superior to results achieved by alternative methods.

Big data is essential for further development of integrative medicine.

To give a short summary on achievements, opportunities and challenges of big data in integrative medicine (IM) and explore the future works on breaking the bottleneck to make IM develop rapidly, this paper presents the growing field of big data from IM, describes the systems of data collection and the techniques of data analytics, introduces the advances, and discusses the future works especially the challenges in this field. Big data is increasing dramatically as the time flies, whatever we face it or not. Big data is evolving into a promising way for deep insight IM, the ancient medicine integrating with modern medicine. We have great achievements in data collection and data analysis, where existing results show it is possible to discover the knowledge and rules behind the clinical records. Transferring from experience-based medicine to evidence-based medicine, IM depends on the big data technology in this great era.

MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier.

MOTIVATION: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. RESULTS: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. AVAILABILITY AND IMPLEMENTATION: An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/. CONTACT: pandaxiaoxi@gmail.com or gzli@tongji.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Factors influencing the quality of preconception healthcare in China: applying a preconceptional instrument to assess healthcare needs.

BACKGROUND: Preconception care is defined as the promotion of the health and well-being of a woman and her partner before pregnancy. Improving preconception health can result in improved reproductive health outcomes. China has issued latest version official guideline for preconception care in 2011. The objective of this cross-sectional study is to determine whether there is a variation in the quality of preconception healthcare services in distinct eastern and northern populations of China, and what factors are associated with such variation. METHODS: A cross-sectional survey using our previously developed preconception instrument was conducted. Women at reproductive age planning for pregnancy were surveyed along with their partners at hospitals during their pre-pregnancy health examination. Data collected include general health/life profiles, pregnancy history, alcohol/tobacco/drug exposures, immunizations, micronutrient supplements and the demands in preconception care. After quality assessment, statistical analysis were applied to evaluate the variations in preconception factors between people from Hebei and Jiangsu Provinces. RESULTS: 3202 women of reproductive age in from eastern province, Jiangsu, and in a northern province, Hebei, participated this study. 2806 of them and their partners have completed the questionnaire, at a rate of 87.6%, 1011 were from Jiangsu and 1795 were from Hebei. Statistical significance was obtained for maternal age (P < 0.001), body mass index (u =13.590, P <0.001), education (χ2 = 916.33, P < 0.001), occupation (χ2 = 901.78, P < 0.001), health status/common disease, immunization status, and need for preconception care. CONCLUSIONS: For a country as large as China, the centralized guideline for standardized preconception healthcare does have a very crucial positive role in reproductive healthcare, but it may not be suited for all populations. Regional authorities should consider the demographics and healthcare needs of the local population and modify the centralized guideline accordingly, as well as provide a better education and professional services for the public, to improve the quality of preconception services at both the regional and the national level.