ObjectiveTo analysis the affecting factors of Acupuncture Deqi by Data Mining. MethodsLiteratures about Acupuncture Deqi, which published from October 1949 to November 2013, were searched from Chinese-language databases (CNKI, WanFang, VIP and CBM) and PubMed database with main keywords "deqi" or "needle sensation" etc. The relational Modern Literatures Database about Acupucture Deqi database was established via Data Enging of Microsoft SQL Server 2005 Express Edition, and correlated documents were excavated via Apriori algorithm in Weka. ResultsThree hundred and thirty-seven studies were selected. Analyzed by Apriori algorithm, frequencies ranking of needle sensation among patients were swelling, numbness, conduction and soreness etc. from high to low and similarly hereinafter; and among health adults were pain, soreness, numbness and heaviness etc. Frequencies ranking of correlation analysis results among patients were heaviness-pain-numbness, soreness-pain-numbness, heaviness-soreness etc. and among health adults were swelling-soreness, heaviness-soreness-numbness, heaviness-soreness etc. ConclusionFunctional status of human body is an important affecting factor of Acupuncture Deqi.
The era of big data has brought a big revolution that will transform the way we live, work, and think. In medical field, as the development of social economics and medicine since 21 century, the human disease spectrum has been changing, the disease type has been increasing, and the complexity of the etiology, diagnosis and treatment of disease have been gradually increasing. In order to improve the healthy level, and explore the law of disease occurrence and development, we should constantly research to find discipline in enormous knowledge by fully mining and using the big medical data. It will be helpful to improve the level medical information management. And it can be supportive to the diagnosis, treatment, clinical practice and decision-making. We did the review under the background of big data, and the mean contact of this review is about the origin, meaning, classification, features of big data as well as the research process, application and future development of data mining, especially clinical data mining.
ObjectiveTo understand the inpatient classification and influence factors of hospitalization expenses, so as to provide basis for hospital management. MethodsThe diagnosis and treatment data of inpatients in a grade A tertiary hospital in 2013 were collected, the percentile method were used to describe the expenses distribution, the K-means clustering method was applied to classify the inpatients, the rank-sum test was utilized to analyze the differences of the costs among different groups, ICD-10 was applied to analyze the diseases distribution, and the median regression was used to analyze the influence factors. ResultsThere were 175 333 inpatients in total. The median of the expenses was 10 016.31 yuan RMB. The inpatients might be classified into seven groups with different expenses (P=0.0001). For inpatients who had no "blood transfusion cost", the top three factors of cost category were operation, laboratory test, examination; for who had "blood transfusion cost", the top three factors of cost category were blood transfusion, laboratory test, examination. There were 2 147, 2 182, 1 499, 1 301, 2 059, 22 and 14 kinds of diseases (ICD-10 four-digit code) respectively among the seven groups. The influence factors could be summarized into patient-related and diagnosis & treatment-related ones. ConclusionThe costs of operation, blood transfusion, laboratory test, and examination affect the inpatients classification greatly. The results could be of help to inform the admission of patients, the expense control and the disease management.
Using optical imaging equipment with different wavelength and computer technology, fundus optical imaging diagnostic techniques can record fundus reflected light, auto fluorescence and emitted light after excitation by external light source in order to observe and analyze the structure and pathological process of retina and choroid. Advances in fundus optical image capture technology (including laser, confocal laser, spontaneous auto-fluorescence, multispectral imaging) and storage and analysis technology, promote this field into a high-definition digital imaging era, with features of rapid, non-invasive, wide-angle three-dimensional multi-level integration, dynamic automatic navigation location tracking and combined application of a variety of optical imaging diagnostic techniques. In order to promote clinical and scientific research of ocular fundus diseases, we need to understand the development trend of optical imaging diagnostic technique, interpret the fundus imaging features appropriately, reasonably chose different inspection techniques, establish standardized diagnosis criteria and continue to expand clinical applications.
ObjectivesTo analyze the development of acupuncture registered trials based on WHO international clinical trial registration platform (ICTRP) in the past 5 years.MethodsWHO ICTRP database was electronically searched to collect acupuncture-related clinical trials registered from January 1st, 2014 to December 31st, 2018. Two reviewers independently screened items, extracted data, and descriptive analysis was performed for the included trials.ResultsThe results showed that there were 1 556 registered clinical trials on acupuncture, and the most registered year was 2017. China was in the main country in applying for acupuncture-related clinical trials, however, the most registered unit was Kyung Hee University in Korea. The trials were mainly interventional research, mostly used randomized, blinded methods, and design modes were mainly based on parallel trials. In clinical trial phase, the majority were in the clinical trial period of treatment of new technologies. The field of clinical research was expected to be on pain in the future.ConclusionsAlthough acupuncture research is currently in a good stage of development, it should still value on the quality and innovative training of relevant trials, strengthen Chinese ties with other countries, focus on regional, domestic and international cooperation, expand research types, and enhance acupuncture applicability.
In the realm of data mining based on modern acupuncture clinical research, the impact of literature features such as literature quality, evidence level, sample size, and clinical efficacy on the quality of data mining outcomes remains uncertain. These issues are significant factors restricting the translational application of data mining research results. We suggest employing both entropy weight and linear weighting techniques to assess the specified indicators. This assessment results in a comprehensive weighted score for acupuncture prescriptions, serving as the foundation for our ensuing data mining endeavors. In this study, migraine research serves as an example to contrast the efficacy of weighted algorithms against that of classical algorithms. The findings demonstrate that the algorithm introduced in this research significantly contributes to studies focusing on the dispersed selection of acupuncture points. Its superiority lies in cluster analysis, where it adeptly discerns potential patterns in the amalgamation of acupoints. This algorithm amalgamates evidence-based acupuncture with data mining processes, providing innovative perspectives that augment the caliber of research in acupuncture data mining. Nonetheless, additional research is essential to corroborate these results.