An Exploratory Data Analysis of Mazu Culture Research Based on Diaolong-Full-Text Database of Ancient Chinese and Japanese Books

Mazu is the most famous goddess of canal transport in China,and one of the three folk beliefs in China.Japan is our neighbor across the sea.As early as 1000 years ago,Japan was influenced by the Mazu ceremonial culture.Through big data analysis,this study conducted database counting,screening,and analysis on the Mazu culture in Diaolong,the full-text database of Chinese and Japanese ancient books.Besides,it explored the hot topics of concern and emotional attitudes,and then analyzed the important role of Mazu culture in the cultural exchange and mutual learning between China and Japan in the new era,with a view to completing the contemporary task of“people-to-people bond”and achieving common development.

Recent Advances in the Study of Ancient Books on Traditional Chinese Medicine

The ancient books on traditional Chinese medicine(TCM) are the source of knowledge for TCM physicians. Therapeutic principles and therapeutic methods for healing many diseases are recorded in these ancient TCM books, providing a huge number of references for modern TCM physicians on conducting diagnosis and administering treatment for different diseases. The ancient TCM books can be dated back thousands of years, and this vast knowledge is recorded in different medical books in the form of text. However, it is difficult to systematically assimilate much information in ancient TCM books. At present, many researchers are applying advanced analytical techniques to analyze the text data in the ancient TCM books. Advanced techniques that have been applied include database construction, cognitive linguistic analysis, fuzzy logic, data mining, and artificial intelligence(AI) technology. There are different characteristics in these advanced analytical techniques. In this study, we comprehensively review recent advances in these techniques applied to the study of ancient TCM books. Furthermore, as AI technology is increasingly utilized in the medical field as well as in the study of ancient TCM books, we also review the application of AI technology to the study of ancient TCM books.

Ancient and modern medication laws of aromatic Chinese medicines in treating angina pectoris based on data mining

Objective To explore ancient and modern medication laws of aromatic Chinese medicines in treating angina pectoris, and to provide new ideas for the clinical treatment.Methods With “angina pectoris” as the key word, ancient books prescriptions and Chinese patent medicines related to angina pectoris were collected from China National Knowledge Infrastructure(CNKI), Traditional Chinese Medicine Database System, Chinese Medicine Prescription Database, New National Proprietary Chinese Medicine(2 nd edition), and Chinese Pharmacopoeia(2020 edition) from January 1, 2015 to December 31, 2021. Core highfrequency aromatic Chinese medicines were defined, and their potential medication rules were analyzed and summarized. Microsoft Access 2010 was used for data management. Data analysis software, including Excel and IBM SPSS Modeler 18.0 were used for drug association rule analysis, and Cytoscape 3.7.2 for visual display.Results There were 67 ancient books prescriptions and 258 Chinese patent medicines containing aromatic Chinese medicines treating angina pectoris collected from relevant databases. In ancient books prescriptions, there were nine aromatic Chinese medicines with the frequency ≥10, and the most commonly used medicine was Danggui(Angelicae Sinensis Radix), followed by Chenpi(Citri Reticulatae Pericarpium). There were 33 aromatic Chinese medicines with the frequency ≥10 in Chinese patent medicines, and the most commonly used medicine was Danshen(Salviae Miltiorrhizae Radix et Rhizoma), followed by Chuanxiong(Chuanxiong Rhizoma) and Sanqi(Notoginseng Radix et Rhizoma). In ancient books prescriptions, the medicines mainly belonged to intenal-warming medicines, Qi-regulating medicines, and blood circulation promoting and blood stasis removing medicines.There were eight medicine pairs with confidence equal to 100% in ancient books prescriptions, the most frequently used pairs were Chuanxiong(Chuanxiong Rhizoma) +Danggui(Angelicae Sinensis Radix), and Xiangfu(Cyperi Rhizoma) + Chenpi(Citri Reticulatae Pericarpium). In Chinese patent medicines, the aromatic Chinese medicine Chuanxiong(Chuanxiong Rhizoma) could be combined with many other Chinese medicines, among which the Confidence and Support of Chuanxiong(Chuanxiong Rhizoma) + Danshen(Salviae Miltiorrhizae Radix et Rhizoma) were at a high level.Conclusion Aromatic Chinese medicines for the treatment of angina pectoris of coronary heart disease are mainly warm, and the flavors are mainly pungent, sweet, and bitter. They mainly access to the liver, gallbladder, and pericardium meridians. The treatment of angina pectoris of coronary heart disease mainly focuses on warming heart pulse, and promoting blood circulation and removing blood stasis.

Intelligent Prescription-Generating Models of Traditional Chinese Medicine Based on Deep Learning

Objective:This study aimed to construct an intelligent prescription-generating(IPG)model based on deep-learning natural language processing(NLP)technology for multiple prescriptions in Chinese medicine.Materials and Methods:We selected the Treatise on Febrile Diseases and the Synopsis of Golden Chamber as basic datasets with EDA data augmentation,and the Yellow Emperor’s Canon of Internal Medicine,the Classic of the Miraculous Pivot,and the Classic on Medical Problems as supplementary datasets for fine-tuning.We selected the word-embedding model based on the Imperial Collection of Four,the bidirectional encoder representations from transformers(BERT)model based on the Chinese Wikipedia,and the robustly optimized BERT approach(RoBERTa)model based on the Chinese Wikipedia and a general database.In addition,the BERT model was fine-tuned using the supplementary datasets to generate a Traditional Chinese Medicine-BERT model.Multiple IPG models were constructed based on the pretraining strategy and experiments were performed.Metrics of precision,recall,and F1-score were used to assess the model performance.Based on the trained models,we extracted and visualized the semantic features of some typical texts from treatise on febrile diseases and investigated the patterns.Results:Among all the trained models,the RoBERTa-large model performed the best,with a test set precision of 92.22%,recall of 86.71%,and F1-score of 89.38%and 10-fold cross-validation precision of 94.5%±2.5%,recall of 90.47%±4.1%,and F1-score of 92.38%±2.8%.The semantic feature extraction results based on this model showed that the model was intelligently stratified based on different meanings such that the within-layer’s patterns showed the associations of symptom–symptoms,disease–symptoms,and symptom–punctuations,while the between-layer’s patterns showed a progressive or dynamic symptom and disease transformation.Conclusions:Deep-learning-based NLP technology significantly improves the performance of IPG model.In addition,NLP-based semantic feature extraction may be vital to further investigate the ancient Chinese medicine texts.