|Year : 2022 | Volume
| Issue : 4 | Page : 548-555
Application of the data mining algorithm in the clinical guide medical records
Xin-Yuan Liu1, Jing-Hua Li1, Ying-Hui Wang1, Lim Weihan2, Yi-Meng Wang1, Ye Tian1, Yan Huang1, Shao-Lei Tian1, Qi Yu1
1 Chinese Medicine Health Intelligent R&D Center, Information Institute of Traditional Chinese Medicine, China Academy of Medical Sciences, Beijing, China
2 Kidney Disease Special Clinic, Singapore Thong Chai Medical Institution, Singapore
|Date of Submission||04-Feb-2021|
|Date of Acceptance||09-Aug-2021|
|Date of Web Publication||21-Jul-2022|
Dr. Qi Yu
Institute of Traditional Chinese Medicine Information, China Academy of Chinese Medical Sciences, 16 Dong Zhimen Nei Nanxiao Street, Dongcheng District, Beijing100700
Source of Support: None, Conflict of Interest: None
Objective: This study analyzed the data of the medical cases in the book, “Clinical Guide Medical records” using a data mining method, to provide a reference for Ye Tianshi's academic thoughts. Methods: We used the web version of the ancient and modern medical records cloud platform to complete distribution statistics, association rules, cluster analysis, and complex network analysis of all the medical records in the “Clinical Guide Medical records.” These methods were used to summarize the baseline data and to identify the core relationship between Chinese medicine diseases and Chinese medicine, as well as the Chinese medicine Classification. Results: A total of 2572 medical records, 3136 visits, and 2879 prescriptions of 1127 traditional Chinese medicines were included in this study. The most common diseases (such as hematemesis), syndromes (such as liver–stomach disharmony), symptoms (such as rapid pulse), disease sites (such as gastric cavity), disease properties (such as Yang deficiency), treatment methods (such as activating Yang), and traditional Chinese medicines (such as Poria cocos) were identified. Furthermore, medicines with a warm, flat, cold, sweet, or bitter taste with its effects on the lungs, spleen, and heart were the most common. The observed effects of the drugs included clearing dampness, promoting diuresis, and strengthening the spleen. The association analysis showed that the associations between TCM diseases and traditional Chinese medicines that had a high confidence were “phlegm and fluid retention–Poria cocos,” “diarrhea–Poria cocos,” etc. The cluster analysis showed that traditional Chinese medicines were classified into five categories. The complex network showed the core relationship between nine high-frequency diseases and nine high-frequency traditional Chinese medicine. Conclusion: This study revealed the most important relationships between traditional Chinese medicines diseases and traditional Chinese medicines and classified the most used traditional Chinese medicines. These findings may help the coming generations of doctors to make accurate diagnoses and treat patients effectively and to improve the clinicians' efficacy in clinical diagnosis and treatment.
Keywords: ”Clinical guide medical records,” data mining, the web version of ancient and modern medical records cloud platform
|How to cite this article:|
Liu XY, Li JH, Wang YH, Weihan L, Wang YM, Tian Y, Huang Y, Tian SL, Yu Q. Application of the data mining algorithm in the clinical guide medical records. World J Tradit Chin Med 2022;8:548-55
|How to cite this URL:|
Liu XY, Li JH, Wang YH, Weihan L, Wang YM, Tian Y, Huang Y, Tian SL, Yu Q. Application of the data mining algorithm in the clinical guide medical records. World J Tradit Chin Med [serial online] 2022 [cited 2022 Dec 1];8:548-55. Available from: https://www.wjtcm.net/text.asp?2022/8/4/548/351511
| Introduction|| |
Ye Tianshi was a famous febrile pathologist in Qing Dynasty, and the book, Clinical Guide Medical Records, is the epitome of his medical thoughts. This book was written by his disciple, Hua Xiuyun. The book is divided into 10 volumes: volumes 1–8, which focus on internal miscellaneous diseases and contain a section for surgery and facial features; volume 9, which focuses on gynecology; and volume 10, which focuses on pediatrics. Volume 10 is followed by a collection of prescriptions that cover a variety of diseases. The impact of this book is transgenerational. Most previous studies focused on a specific disease, drug, or treatment. Moreover, none of the previous studies investigated these with reference from a book.
Data mining, also known as “knowledge discovery,” is an important process for extracting useful information from large data sets., Data mining has been used widely in the general data mining field. In recent years, data mining has been used widely in the field of traditional Chinese medicine (TCM). For example, Sun et al. conducted a study on data mining and network pharmacology, in which they summarized the experience of treatment of mycoplasma pneumonia with TCM. Wang et al. used association rules to analyze the drug rule of treatment of tumors with TCM, which provided an entry point for drug monitoring. Furthermore, Xia et al. used association analysis, cluster analysis, and complex network analysis to summarize the pathogenesis, treatment methods, and core prescriptions of TCM treatment of chronic kidney disease.
In this study, all medical records in Clinical Guide Medical Records were collected, and data mining methods were used to conduct statistics on the distribution of TCM diseases, disease locations, disease properties, syndrome types, treatment methods, TCM, four qi, five flavors, meridians, and efficacy. Furthermore, an association analysis and complex network analysis of “traditional Chinese medicine diseases-traditional Chinese medicine” were used to perform a cluster analysis of TCM, to provide references for the application of Clinical Guide Medical Records.
| Data Mining Methods and Tools|| |
All medical records in the Clinical Guide Medical Records were collected and used to construct the database of the Clinical Guide Medical Records. The database was then imported into the web version of the ancient and modern medical records cloud platform (http://www.yiankb.com) in batches. Next, the TCM data were standardized, and the standard database was imported into the analysis pool. The data mining section of the ancient and modern medical records cloud platform was used to perform frequency statistics, association analysis, cluster analysis, and complex network analysis.
Data collection and regulation
Data were collected from the Clinical Guide Medical Records (China traditional Chinese Medicine Publishing House, 2008,10), which contains 2572 medical records and 3136 visits. The medical records were entered into Microsoft Excel 2016, by means of double recording, including general patient information (name, sex, age, etc.), clinical manifestations, TCM diagnosis, TCM syndromes, treatment methods, prescription names, traditional Chinese medicine, original medical records, etc. Next, we completed the TCM prescriptions according to their composition (e.g., Xiaoyao San, Guipi, etc.) and obtained 2879 prescriptions that were used to establish the Clinical Guide Medical Records database. Next, we imported the Clinical Guide Medical Records database into the “ancient and modern medical case cloud platform” database and used the medical record standardization function to standardize the TCM data. The reference standards included Pharmacopoeia of the People's Republic of China (2015 Edition), Chinese Materia Medica (Shanghai Science and Technology Press, 1999), Dictionary of Chinese Materia Medica (Shanghai Science and Technology Press, 2006), and Chinese Pharmacy (Gao Xuemin, China Press of Traditional Chinese Medicine, 2002). The “Jin Lingzi” has been standardized to “toosendan fruit,” “Guang Pi” to “tangerine peel,” and “Nan Zao” to “jujube,” and so on.
The ancient and modern medical records cloud platform
The web version of the ancient and modern medical records cloud platform (http://www.yiankb.com) was developed by the Institute of Chinese Medicine Information, China, Academy of Chinese Medical Sciences. It covers more than 400,000 structured medical records, based on retrieved medical records, collected services, and data mining methods, such as frequency statistics, association analysis, mutual information analysis, network pharmacology analysis, complex network analysis, cluster analysis, etc., Furthermore, it is an intelligent means by which famous doctors' experiences are handed down to the next generations.,
Distribution statistics refers to the categorization of all population units into groups using statistical grouping, to form a distribution of the overall units among the groups. The number of distribution units in each group is called the number or frequency. The ratio of the number in each group to the total number of the group is called proportion. The “data mining” section of the web version of the ancient and modern medical records cloud platform was used to perform statistical analyses of TCM diseases, syndrome types, treatment methods, syndrome elements (disease sites disease properties), medicine, four qi, five flavors, meridians, and effects of the drugs.
Association analysis is an important algorithm for discovering interesting connections between item sets in a large amount of data through association rules. It is also used widely in the field of Chinese medicine. Association rules can be expressed in the form of A → B, where transaction A is called the front item set and transaction B is called the latter item set. There are three concepts related to association rules, namely support, confidence, and lift. Support is the ratio of the sum of the front item (set A) and the latter item (set B) to all item sets. Confidence is the proportion of the sum of the front item (set A) in the latter item (set B) in the front item (set A). Lift is the ratio of the sum of the front item (set A) and the latter item (set B) to the latter item (set B). When the lift is above 1, it means that A and B are more related when they do not intersect., In this study, the collection of Chinese medicine diseases was defined as transaction A and the collection of Chinese medicine as transaction B, to explore the relationship between diseases and TCM. Furthermore, to explore the relationship between diseases and traditional Chinese medicine, we used the “data mining-association analysis” function of the web version of the ancient and modern medical records cloud platform. We selected “Chinese Medicine Disease” for the former item and “Chinese Medicine” for the latter item.
Cluster analysis is a multivariate statistical method. When the classification is unknown, the objects of interest are divided into multiple classes of similar objects according to certain characteristics., Data of the same category have a high degree of similarity, but the similarity of data between different categories is very low. Cluster analysis is helpful to preprocess ambiguous original data and identify the universal and basic problems for further studies. The “Data Mining-Clustering Analysis” of the web version of the ancient and modern medical record cloud platform was used to cluster the top 20 high-frequency Chinese medicines and analyze the most used types of Chinese medicines by Ye.
Complex network analysis
The complex network analysis makes good use of graphical visualization methods to show the relationship between nodes and edges. In this study, nodes represent diseases and TCM. In the prediction of the relationship between “disease treated with TCM” and “traditional Chinese medicine,” the larger the number of the connection between the nodes, the higher the use of the Chinese medicine to treat the disease. The function of “data mining-complex network analysis” in the web version of the ancient and modern medical records cloud platform was used. Furthermore, “diseases treated with TCM” were chosen as the former item and “traditional Chinese Medicine” as the latter item. As a result, there was a high frequency of use between different TCM diseases and TCM.
| Results|| |
There are 88 diseases in the Clinical Guide Medical Records, with a frequency of 3,136. Among them, the frequency of hematemesis is the highest (236), with a proportion (frequency/total number of cases) of 7.53%, in a decreasing order of frequency. The top 10 diseases are shown in [Table 1].
|Table 1: Traditional Chinese Medicine diseases in the Clinical Guide Medical Records|
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There are 784 syndromes in the Clinical Guide Medical Records, with a frequency of 3,145. Among them, the frequency of liver–stomach disharmony is the highest (89), with a proportion (frequency/total number of cases) of 2.84%. The top 10 syndromes are shown in [Table 2].
|Table 2: Top 10 Traditional Chinese Medicine syndromes in the Clinical Guide Medical Records|
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There are 11,357 symptoms in the Clinical Guide Medical Records, with a frequency of 12,513. Among them, the frequency of rapid pulse is the highest (25), with a proportion (frequency/total number of cases) of 0.80%. The top 10 symptoms are shown in [Table 3].
There are 11 disease sites in the Clinical Guide Medical Records, with a frequency of 420. Among them, the frequency of gastric cavity is the highest (116), with a proportion (frequency/total number of cases) of 3.69%. The disease sites are shown in [Table 4].
There are 28 disease properties in the Clinical Guide Medical Records, with a frequency of 915. The frequency of Yang deficiency is the highest (113), with a proportion (frequency/total number of cases) of 3.59%. The top 10 disease properties are shown in [Table 5].
|Table 5: Top 10 disease properties in the Clinical Guide Medical Records|
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There are 1325 treatment methods in the Clinical Guide Medical Records, with a frequency of 1519. Among them, the frequency of activating yang is the highest (12), with a proportion (frequency/total number of cases) of 0.38%. The top 10 treatment methods are shown in [Table 6].
Traditional Chinese medicine
Frequency of traditional Chinese medicines
There are 1127 kinds of TCM in the Clinical Guide Medical Records, with a frequency of 19,557. Among them, the frequency of Poria cocos is the highest (986), with a proportion (frequency/total number of cases) of 31.44%. The top 20 traditional Chinese medicines are shown in [Table 7] in decreasing order of frequency.
|Table 7: Traditional Chinese Medicine types in the Clinical Guide Medical Records (Top 20)|
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Attributes of traditional Chinese medicines
In the Clinical Guide Medical Records, the Chinese medicines with a warm, flat, or cold nature and sweet, bitter, or spicy taste, are the most used. The most used TCMs belong to the lungs, spleen, and heart meridians, and their main effects include clearing dampness, promoting diuresis, and strengthening the spleen. The top 10 attributes are shown in [Table 8].
|Table 8: Traditional Chinese Medicine attributes in the Clinical Guide Medical Records|
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Traditional Chinese medicine diseases–traditional Chinese medicine association rules
We analyzed the association rules between TCM diseases and TCM in the Clinical Guide Medical records using a confidence above 0.01 and a support above 0.01. The top 10 combinations of TCM diseases and TCM are shown in [Table 9].
|Table 9: Top 10 traditional Chinese medicine diseases– traditional Chinese medicine association rules in the Clinical Guide Medical records|
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Clustering of traditional Chinese medicine
We performed a cluster analysis of the 20 most frequently used TCMs in the Clinical Guide Medical Records. To get a horizontal tree diagram, we selected “traditional Chinese medicine,” “Euclidean distance,” and “longest clustering method” [Figure 1].
|Figure 1: Cluster analysis of the top 20 drugs in the Clinical Guide Medical Records|
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Taking a distance ≥25 as the boundary, the high-frequency traditional Chinese medicines in the Clinical Guide Medical records were divided into five categories as follows:
- The first category: Poria cocos
- The second category: Ginseng
- The third category: Bitter apricot seed, Pinellia ternata, Tangerine peel, and Magnolia officinalis
- The fourth category: Radix Paeoniae alba, prepared licorice, Cassia twig, Bighead atractylodes rhizome, and Angelica sinensis
- The fifth category: Poria with hostwood, Radix Rehmanniae, Donkey-hide gelatin, Ophiopogon japonicus, Liquorice, Rhizoma coptidis, Moutan bark, Prepared rehmannia root, and Schisandra chinensis.
TCM diseases–Traditional Chinese medicine complex network
We performed complex network analyses of “TCM diseases–traditional Chinese medicine” in the Clinical Guide Medical records. We selected an edge weight >40 to get a variety of diseases and their core traditional Chinese medicine combinations [Figure 2].
|Figure 2: TCM diseases–TCM Complex Network in the Clinical Guide Medical records|
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The results of the complex network analysis showed nine diseases with high frequencies and nine high-frequency traditional Chinese medicines, showing the relationship between the nine major diseases and core traditional Chinese medicines. The core traditional Chinese medicines for treating malaria, puerperal disease, hematemesis, and cough are Radix Rehmanniae, Ophiopogon japonicus, Poria with hostwood, tangerine peel, ginseng, Cassia twig, Prepared licorice, Poria cocos, and Bitter apricot seed. The core traditional Chinese medicines for swelling are tangerine peel, ginseng, prepared licorice, Poria cocos, Cassia twig and bitter apricot seed. The core traditional Chinese medicines for consumptive disease and dysentery are Radix Rehmanniae, Poria cocos, prepared licorice, Tangerine peel, Ophiopogon japonicus, Poria with hostwood, ginseng, and Cassia twig. The core traditional Chinese medicines for phlegm and fluid retention and diarrhea are bitter apricot seed, Poria cocos, prepared licorice, tangerine peel, ginseng, Poria with hostwood, and Cassia twig.
| Discussion|| |
As one of the “four masters of febrile disease,” Ye was good at treating several febrile diseases. Several medical records of Ye's treatment methods of febrile diseases are recorded in the Clinical Guide Medical records. According to disease statistics, the most common diseases in the Clinical Guide Medical records are hematemesis, malaria, cough, consumptive disease, puerperal disease, dysentery, swelling, diarrhea, phlegm and fluid retention, and arthralgia. Moreover, the most common syndromes are liver–stomach disharmony, dampness–heat, and Yin deficiency. The most common clinical symptoms involve the tongue and pulse, such as heat, rapid pulse, difficulty in defecation, alternate chills and fever, left rapid pulse, and stringy pulse. The most common disease sites are the gastric cavity, spleen, and kidney. The disease properties are cold, heat, deficiency, or excess, with the most common being Yang deficiency and Yin deficiency. The most used treatment methods include activating Yang, relaxation-inducing sweet herbs, acid and bitter release heat, and Xuan tong. Therefore, it can be speculated that when Ye's treatment of hematemesis, malaria, cough, consumptive disease, and other diseases is used, deficiency and heat symptoms, and symptoms limited to the spleen and stomach are more common. Moreover, they can be used to treat Yang depression caused by liver depression or dampness–heat by promoting Yang; the sweet and soothing product can be used to nourish Yin, and the sour and bitter product can be used for heat.
Poria cocos, ginseng, prepared licorice, Radix Paeoniae alba, Cassia twig, Poria with hostwood, tangerine peel, Pinellia ternata, Radix Rehmanniae, and 20 other Chinese medicines are the most used in the Clinical Guide Medical Records, including the Chinese herbal formula: Liu-Junzi, Gui-Zhi, Sheng-Mai, Si-Wu, Huang-Lian E-Jiao, etc. As effects, these medicines mainly replenish qi, strengthen the spleen, reconcile Ying wei, nourish Yin and blood, promote qi and diuresis, etc.; these effects are consistent with those of the assessed Chinese medicines. The Traditional Chinese medicines have a warm, flat, cold, or slightly cold nature and a sweet, bitter, or spicy taste. Chinese medicines are mostly for to the lung, spleen, and heart meridians. This led us to the conclusion that Ye used sweet-cold products to nourish Yin; bitter-cold products to clear heat and purge fire; spicy-warm products to dispel cold; bitter-cold products to spread wind-heat; and bitter-warm products to dispel cold and humidity.
The association analysis showed that associations with Poria cocos have the highest confidence (phlegm and fluid retention–Poria cocos, phlegm and fluid retention–Cassia twig, phlegm and fluid retention–Pinellia ternata). However, its lift is lower than that of Cassia twig and Pinellia ternata, indicating that Poria cocos is the most used TCM for the treatment of phlegm and fluid retention, and it can also be used for other diseases. On the contrary, the findings indicate that Cassia twig and Pinellia ternata that have a lower confidence and higher lift can be used especially for the treatment of phlegm and fluid retention. Poria cocos can also be used for the treatment of diarrhea, vomiting, wood by soil, and puerperal disease [Table 9]. Tangerine peel can be used specially for the treatment of diarrhea and Ginseng can be used especially for the treatment of wood by soil.
Poria cocos is sweet, tasteless, and flat, and belongs to the heart, lung, spleen, and kidney meridians. The effects of clearing dampness and promoting diuresis, strengthening the spleen, and calming the heart are to the bases of treatment of edema, phlegm retention, palpitation, deficiency of splenic components, nutrient deficiency, diarrhea, restlessness, insomnia, etc. Modern pharmacological studies have proven that Poria cocos has anti-tumor properties, protects the liver, promotes diuresis, strengthens immunity, restores intestinal function, etc.,, The frequency of Poria cocos use in the book is 986, which shows that Ye used it to treat several diseases. Cassia twig provides a sweet-warm effect in reversing spleen–Yang deficiency and transporting water. Pinellia ternata is used to manage dryness and dampness, and to eliminate phlegm. Meanwhile, he used tangerine peel to manage spleen issues, especially stopping diarrhea caused by a deficiency of the spleen components. He used ginseng to replenish the patient's vital energy in the management of deficiency of spleen components caused by altered liver function. Ye's methods can be used as a reference for other diseases.
Regarding the cluster analysis, Poria cocos was able to clear dampness, promote diuresis, strengthen the spleen functions, and calm the heart in the management of heart and spleen diseases, such as edema, deficiency of spleen components, and palpitation. Second, ginseng helps in replenishing vital energy, restoring the pulse, preventing prostration, tonifying the spleen and lungs, increasing fluid levels, nourishing blood, and calming nerves—these can be used to treat qi deficiency and other hematologic and spleen diseases. Third, bitter apricot seed, tangerine peel, Pinellia ternata, and Magnolia officinalis promote diuresis, extract phlegm, and relieve cough; these can be used to treat lung diseases. Fourth, Radix Paeoniae alba, prepared licorice, Cassia twig, Atractylodes, Angelica sinensis, and Chinese herbal formulae (Dang-gui Shao-yao) help in tonifying the blood and strengthening the spleen. Furthermore, Cassia twig supplement Yang and promote diuresis prepared licorice help in managing spleen and stomach diseases like altered liver and spleen function, qi depression, and blood stagnation syndrome. Fifth, Poria with hostwood, Radix Rehmanniae, donkey-hide gelatin, Ophiopogon japonicus, Licorice, Rhizoma coptidis, Moutan bark, prepared Rehmannia root, and Schisandra chinensis helped to nourish Yin, clear heat, replenish qi, and nourish blood; as such, it can be used to treat Yin deficiency.
Regarding the complex network analysis, five Chinese medicines (tangerine peel, ginseng, Cassia twig, prepared licorice, and Poria cocos) are all used in nine common diseases. It can be speculated that these five medicines are also used for other diseases. Tangerine peel, ginseng, prepared licorice, and Poria cocos strengthen the spleen, while Cassia twig can dredge yang and transform qi, showing that Ye Tianshi started with the spleen and stomach during treatment, for diseases requiring spleen strengthening and yang dredging. Radix Rehmanniae and Ophiopogon japonicus can nourish Yin and are used frequently, suggesting that there are many medical records of Yin deficiency in the Clinical Guide Medical records. Poria with hostwood tends to calm the heart and soothe the nerves, while bitter apricot seed clears the lungs and stops cough; the latter can be used or not in the management of other syndromes.
| Conclusion|| |
This study aimed to extract useful information from the book, “Clinical Guide Medical Records” through data mining and used the method of distribution statistics to have a more comprehensive understanding of the medical records in the book. Association rules and a complex networks analysis helped us to identify the most important connection between traditional Chinese medicine diseases and traditional Chinese medicine, while the cluster analysis helped us to classify the most used traditional Chinese medicines. These findings will help future doctors to make full use of this information in making accurate diagnoses and treating patients effectively. The method in this paper can also be used to excavate other ancient books and obtain more useful knowledge that may be found in them. This will help to improve clinical diagnosis and treatment.
Liu Xinyuan wrote the manuscript. Yu Qi is the corresponding author. Wang Yinghui and Li Jinghua directed the study conception and design. Wang Yimeng and Tian Ye participated in the construction of the database. Huang Yan conducted the experiments.
Financial support and sponsorship
This study was supported by the “National Natural Science Foundation of China: Research on the discovery of key diagnosis and treatment elements and clinical optimization decision of spleen and stomach diseases based on deep learning (NO: 81873200)” and the “Construction and application of an intelligent early warning system for TCM clinical drug contraindications based on rule engine (NO: ZZ150321).”
Conflicts of interest
There are no conflicts of interest.
| References|| |
Fan Z, Da L, Yujuan Z, Wenhui C. Recent research status of clinical guide medical records. J Liaoning Univ Tradit Chin Med 2016;18:144-6.
Wang D, Miwa T, Morikawa T. Big trajectory data mining: A survey of methods, applications, and services. Sensors (Basel) 2020;20:4571.
Hsu A, Khoo W, Goyal N, Wainstein M. Next-Generation Digital Ecosystem for Climate Data Mining and Knowledge Discovery: A Review of Digital Data Collection Technologies. Front Big Data. 2020 Sep 10;3:29.
Sun JH, Sun F, Yan B, Li JY, Xin L. Data mining and systematic pharmacology to reveal the mechanisms of traditional Chinese medicine in Mycoplasma pneumoniae
pneumonia treatment. Biomed Pharmacother 2020;125:109900.
Wang XX, Han YY, Shi GY, Hu LM, Lu P, Liu F. Analysis and monitoring of traditional Chinese medicines for tumor based on data mining. Zhongguo Zhong Yao Za Zhi 2020;45:444-50.
Xia P, Gao K, Xie J, Sun W, Shi M, Li W, et al.
Data mining-based analysis of Chinese medicinal herb formulae in chronic kidney disease treatment. Evid Based Complement Alternat Med 2020;2020:9719872.
Li JH, Wang YH, Li ZY, Yu Q,Tian Y, Wang YM, et al
. The application and practice of intelligent technology in the inheritance of famous doctors. Int J Tradit Chin Med 2020;542:1049-1053.
Weibin W, Jinghua L, Yinghui W, Zongyou L, Ye T, Junwen W, et al
. Study on the regularity of traditional Chinese medicine in the treatment of bacterial upper respiratory tract infection. Chin J Tradit Chin Med Inf 2019;26:98-102.
Micheline K. The Concept and Technology of Data Mining [M]. Vol. 3-4. Beijing: Machinery Industry Press; 2012.
Leem J, Jung W, Kim Y, Kim B, Kim K. Exploring the combination and modular characteristics of herbs for alopecia treatment in traditional Chinese medicine: An association rule mining and network analysis study. BMC Complement Altern Med 2018;18:204.
K. Kala. An optimization based algorithm for association rule mining in large databases. Int J Recent Technol Eng 2019;8:3746-3754.
Souza-Dantas VC, Dal-Pizzol F, Tomasi CD, Spector N, Soares M, Bozza FA, et al.
Identification of distinct clinical phenotypes in mechanically ventilated patients with acute brain dysfunction using cluster analysis. Medicine (Baltimore) 2020;99:e20041.
Wang HX, Mo S, Yang L, Wang PP, Su KH, Xiong YQ, et al
. Effectiveness associated with different therapies for senile osteoporosis: A network meta-analysis. J Tradit Chin Med 2020;40:17-27.
Liu J, Han YJ, Zhao TY, Li WR, Chen B, Guo Y, et al.
Rule of acupoint combination in acupuncture treatment of hiccup: A study based on complex network analysis. Zhen Ci Yan Jiu 2019;44:766-71.
Xinyuan L. An analysis of the emergence and spread of the discussion on febrile disease by Ye Tianshi, a famous doctor in the south of the Yangtze River in the Qing Dynasty. Hubei J Tradit Chin Med 2020;42:46-50.
Hui Z, Jifeng T, Jin Z, Qin You R, Jiangang Y. Analysis and experience of Xiao Banxia plus Fuling decoction in the treatment of chemotherapy-related nausea and vomiting. Emerg Tradit Chin Med 2017;26:1124-1125+1128.
Xu H, Wang Y, Jurutka PW, Wu S, Chen Y, Cao C, et al.
16α-Hydroxytrametenolic acid from Poria cocos
improves intestinal barrier function through the glucocorticoid receptor-mediated PI3K/Akt/NF-κB pathway. J Agric Food Chem 2019;67:10871-9.
Wang YX, Zhang SS, Zhou Q, Meng M, Chen WW. Efficacy of Shenlingbaizhu formula on irritable bowel syndrome: A systematic review. J Tradit Chin Med 2020;40:897-907.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9]