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.
Background Nowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy. Objective This study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases. Methods The relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data. Results The data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%. Conclusions The main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.
Objectives. To conduct a comprehensive analysis of scientific outputs in 2011–2021 regarding the rehabilitative effects of acupuncture on diseases. Methods. The study was conducted in the form of knowledge graph and data visualization, with data being drawn from the Web of Science Core Collection database. Results. Articles and reviews were the dominant types; China, Guangzhou University of Chinese Medicine and Medicine ranked was the active country, institution, and journal, respectively, in terms of issued articles. Systematic reviews and the meta-analyses of stroke and pain were extensively carried out in the past decade, whose principal interventions were manual acupuncture, electroacupuncture, scalp acupuncture, and dry needling correspondingly at Baihui (DU20) and Zusanli (ST36). And most frequently utilized rehabilitation assessment criteria were the Fugl-Meyer Assessment Scale and the Barthel Index. More recently, motor function and chronic obstructive pulmonary disease have captured researchers’ attention, which might be the futuristic frontier. Conclusions. This article provided a relatively panoramic picture of the scientific outputs in acupuncture for disease rehabilitation, which may help readers embrace the heated topic and grasp the recent research focus on this field.
Objective Due to the complexity of face images, tongue segmentation is susceptible to interference from uneven tongue texture, lips and face, resulting in traditional methods failing to segment the tongue accurately. To address this problem, RAFF-Net, an automatic tongue region segmentation network based on residual attention network and multiscale feature fusion, was proposed. It aims to improve tongue segmentation accuracy and achieve end-to-end automated segmentation. Methods Based on the UNet backbone network, different numbers of ResBlocks combined with the Squeeze-and-Excitation (SE) block was used as an encoder to extract image layered features. The decoder structure of UNet was simplified and the number of parameters of the network model was reduced. Meanwhile, the multiscale feature fusion module was designed to optimize the network parameters by combining a custom loss function instead of the common cross-entropy loss function to further improve the detection accuracy. Results The RAFF-Net network structure achieved Mean Intersection over Union (MIoU) and F1-score of 97.85% and 97.73%, respectively, which improved 0.56% and 0.46%, respectively, compared with the original UNet; ablation experiments demonstrated that the improved algorithm could contribute to the enhancement of tongue segmentation effect. Conclusion This study combined the residual attention network with multiscale feature fusion to effectively improve the segmentation accuracy of the tongue region, and optimized the input and output of the UNet network using different numbers of ResBlocks, SE block, multiscale feature fusion and weighted loss function, increased the stability of the network and improved the overall effect of the network.
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