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.

Genome-wide long non-coding RNA association study on Han Chinese women identifies lncHSAT164 as a novel susceptibility gene for breast cancer.

BACKGROUND: Single-nucleotide polymorphisms (SNPs)-associated genes and long non-coding RNAs (lncRNAs) can contribute to human disease. To comprehensively investigate the contribution of lncRNAs to breast cancer, we performed the first genome-wide lncRNA association study on Han Chinese women. METHODS: We designed an lncRNA array containing >800,000 SNPs, which was incorporated into a 96-array plate by Affymetrix (CapitalBio Technology, China). Subsequently, we performed a two-stage genome-wide lncRNA association study on Han Chinese women covering 11,942 individuals (5634 breast cancer patients and 6308 healthy controls). Additionally, in vitro gain or loss of function strategies were performed to clarify the function of a novel SNP-associated gene. RESULTS: We identified a novel breast cancer-associated susceptibility SNP, rs11066150 (Pmeta = 2.34 × 10-8), and a previously reported SNP, rs9397435 (Pmeta = 4.32 × 10-38), in Han Chinese women. rs11066150 is located in NONHSAT164009.1 (lncHSAT164), which is highly expressed in breast cancer tissues and cell lines. lncHSAT164 overexpression promoted colony formation, whereas lncHSAT164 knockdown promoted cell apoptosis and reduced colony formation by regulating the cell cycle. CONCLUSIONS: Based on our lncRNA array, we identified a novel breast cancer-associated lncRNA and found that lncHSAT164 may contribute to breast cancer by regulating the cell cycle. These findings suggest a potential therapeutic target in breast cancer.

How much space of the spinal canal should be restored by hoisting the vertebrae-OPLL complex for sufficient decompression in anterior controllable antedisplacement and fusion? A multicenter clinical radiological study.

BACKGROUND CONTEXT: Anterior controllable antedisplacement and fusion (ACAF) is a novel surgical technique for the treatment of ossification of the posterior longitudinal ligament (OPLL). Its prognostic factors for decompression have not been well studied. Additionally, no detailed radiological standard has been set for hoisting the vertebrae-OPLL complex (VOC) in ACAF. PURPOSE: To identify the possible prognostic factors for decompression outcomes after ACAF for cervical OPLL, to determine the critical value of radiological parameters for predicting good outcomes, and to establish a radiological standard for hoisting the VOC in ACAF. STUDY DESIGN: This was a retrospective multicenter study. PATIENT SAMPLE: A total of 121 consecutive patients with OPLL who underwent ACAF at a point between January 2017 and June 2018 at any one of seven facilities and were monitored for at least 1 year afterward were enrolled in a multicenter study. OUTCOME MEASURES: Japanese Orthopedic Association (JOA) scores, recovery rate (RR) of neurologic function, and surgical complications were used to determine the effectiveness of ACAF. METHODS: Patients were divided into two groups according to their RR for neurologic function. Patients with an RR of ≥50% and an RR of <50% were designated as having good and poor decompression outcomes, respectively. The relationship between various possible prognostic factors and decompression outcomes was assessed by univariate and multivariate analysis. The receiver operating characteristic curve was used to determine the optimal cutoff value of the radiological parameters for prediction of good decompression outcomes. Next, the patients were redivided into three groups according to the cutoff value of the selected radiological parameter (postoperative anteroposterior canal diameter [APD] ratio). Patients with postoperative APD ratios of ≤80.7%, 80.7%-100%, and ≥100% were defined as members of the incomplete, optimal, and excessive antedisplacement groups, respectively. Differences in decompression outcomes among the three groups were compared to verify the reliability of the postoperative APD ratio and assess the necessity of excessive antedisplacement. RESULTS: Multivariate logistic regression analysis showed that patients' age at surgery (odds ratio [OR]=1.18; 95% confidence interval [CI]=1.08-1.29; p<.01) and postoperative APD ratio (OR=0.83; 95% CI=0.77-0.90; p<.01) were independently associated with decompression outcomes. The optimal cutoff point of the postoperative APD ratio was calculated at 80.7%, with 86.2% sensitivity and 73.5% specificity. There were no significant differences in the postoperative JOA scores and RRs between the excessive antedisplacement group and optimal antedisplacement group (p>.05). However, a lower incidence of cerebrospinal fluid leakage and screw slippage was observed in the optimal antedisplacement group (p<.05). CONCLUSIONS: Patients' age at surgery and their postoperative APD ratio are the two prognostic factors of decompression outcomes after ACAF. The postoperative APD ratio is also the most accurate radiological parameter for predicting good outcomes. Our findings suggest that it is essential for neurologic recovery to restore the spinal canal to more than 80.7% of its original size (postoperative APD ratio >80.7%), and restoration to less than its original size (postoperative APD ratio <100%) will help reduce the incidence of surgical complications. This may serve as a valuable reference for establishment of a radiological standard for hoisting the VOC in ACAF.

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.

Midazolam prevents motor neuronal death from oxidative stress attack mediated by JNK-ERK pathway.

Midazolam is a sedative used by patients with mechanical ventilation. However, the potential clinical value is not fully explored. In this report, we made use of a neuroblastoma-spinal cord hybrid motor neuron-like cell line NSC34, and elucidated the potential role of Midazolam on these cells under the insult of oxidative stress. We found the protective effect of Midazolam on motor neurons against cytotoxicity induced by the combination of oligomycin A and rotenone (O/R) or phenylarsine oxide. The characteristics of apoptosis, such as the ratio of TUNEL+ cells or the expression level of cleaved Caspase-3, was decreased by 22 or 45% in the presence of Midazolam. Furthermore, this effect was correlated with the JNK-ERK signaling pathway. Either phosphorylation of ERK or JNK was positively or negatively modulated with the treatment of Midazolam in NSC34 cells attacked by reactive oxygen species. Meanwhile, inhibition or activation of the JNK-ERK pathway regulated the protective effect of Midazolam on NSC34 cells with oxidative stress insult. Collectively, this study elucidated a previously unidentified clinical effect of Midazolam, and put forward the great promise that Midazolam may be considered as a potential candidate to the treatment of motor neuron disease.

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.

Facial Color Management for Mobile Health in the Wild.

Nowadays, mobile technologies have changed the patient routine health care and management. With a large amount of mobile health applications developed, massive and valuable health data are possibly collected with a smart mobile phone in hand. Facial color images are recently proved to be available and effective for health condition diagnosis both in modern medicine and ancient medicine perspectives. One significant issue of facial color health condition diagnosis system is color management, in which its primary procedure is to obtain reliable and device-independent facial color images in the wild. The solution is known as utilizing color correction technology to recover the intrinsic color properties of facial skin. However, current color correction approaches are hard to meet the need of mobile health management in the wild, due to some limitations of precision-challenged algorithm, inconvenient color imaging device, strong scenario assumption and so forth. Therefore, in this paper, we consider several facial skin color characteristics and show that it is valuable to build facial color related correction model for facial color images in the wild. Then we propose two kinds of facial color correction strategies to realize the facial color management of mobile health in the wild. The first one is reference-based approach, and the other one is skin-based approach without requirement of colorchecker. Experimental results with qualitative and quantitative assessments on the indoor and outdoor scenarios demonstrate that the proposed reference-based approach is more outstanding than our previous method and other color constancy methods. In addition, given a facial color image only, the skin-based method can still achieve effective results compared with other color constancy methods.

Cough event classification by pretrained deep neural network.

BACKGROUND: Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. METHOD: The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. RESULTS: The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. CONCLUSIONS: In this paper, we tried pretrained deep neural network in cough classification problem. Our results showed that comparing with the conventional GMM-HMM framework, the HMM-DNN could get better overall performance on cough classification task.

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.

Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.

BACKGROUND: It has become a very important and full of challenge task to predict bacterial protein subcellular locations using computational methods. Although there exist a lot of prediction methods for bacterial proteins, the majority of these methods can only deal with single-location proteins. But unfortunately many multi-location proteins are located in the bacterial cells. Moreover, multi-location proteins have special biological functions capable of helping the development of new drugs. So it is necessary to develop new computational methods for accurately predicting subcellular locations of multi-location bacterial proteins. RESULTS: In this article, two efficient multi-label predictors, Gpos-ECC-mPLoc and Gneg-ECC-mPLoc, are developed to predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. The two multi-label predictors construct the GO vectors by using the GO terms of homologous proteins of query proteins and then adopt a powerful multi-label ensemble classifier to make the final multi-label prediction. The two multi-label predictors have the following advantages: (1) they improve the prediction performance of multi-label proteins by taking the correlations among different labels into account; (2) they ensemble multiple CC classifiers and further generate better prediction results by ensemble learning; and (3) they construct the GO vectors by using the frequency of occurrences of GO terms in the typical homologous set instead of using 0/1 values. Experimental results show that Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently predict the subcellular locations of multi-label gram-positive and gram-negative bacterial proteins respectively. CONCLUSIONS: Gpos-ECC-mPLoc and Gneg-ECC-mPLoc can efficiently improve prediction accuracy of subcellular localization of multi-location gram-positive and gram-negative bacterial proteins respectively. The online web servers for Gpos-ECC-mPLoc and Gneg-ECC-mPLoc predictors are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/gpos-ecc-mploc/ and http://biomed.zzuli.edu.cn/bioinfo/gneg-ecc-mploc/ respectively.

A hybrid imputation approach for microarray missing value estimation.

BACKGROUND: Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate. METHODS: In this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. Meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step. RESULTS: We evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of Normalized Root Mean Square Error (NRMSE), especially for datasets with large missing rates and less complete genes. CONCLUSIONS: It is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.

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.

High mean water vapour pressure promotes the transmission of bacillary dysentery.

Bacillary dysentery is an infectious disease caused by Shigella dysenteriae, which has a seasonal distribution. External environmental factors, including climate, play a significant role in its transmission. This paper identifies climate-related risk factors and their role in bacillary dysentery transmission. Harbin, in northeast China, with a temperate climate, and Quzhou, in southern China, with a subtropical climate, are chosen as the study locations. The least absolute shrinkage and selectionator operator is applied to select relevant climate factors involved in the transmission of bacillary dysentery. Based on the selected relevant climate factors and incidence rates, an AutoRegressive Integrated Moving Average (ARIMA) model is established successfully as a time series prediction model. The numerical results demonstrate that the mean water vapour pressure over the previous month results in a high relative risk for bacillary dysentery transmission in both cities, and the ARIMA model can successfully perform such a prediction. These results provide better explanations for the relationship between climate factors and bacillary dysentery transmission than those put forth in other studies that use only correlation coefficients or fitting models. The findings in this paper demonstrate that the mean water vapour pressure over the previous month is an important predictor for the transmission of bacillary dysentery.

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.