OBJECTIVE: To analysis the medication characteristics of the prescriptions issued via open channel by the National and Provincial Health Committee and the State Administration of Traditional Chinese Medicine in treating coronavirus disease 2019 (COVID-19). METHODS: We collected the data of traditional Chinese medicine related to treatment plans published by the National and Provincial Health Committee and the State Administration of Traditional Chinese Medicine from the start of COVID-19 outbreak in Wuhan to February 19, 2020. The frequency analysis, cluster analysis and correlation analysis were performed. RESULTS: The study collected 4 national and 34 regional prevention and treatment plans, 578 items, 84 traditional Chinese formulations, 60 Chinese patent medicines, and 230 Chinese herbs. The high frequently used herbs were , , , and . The commonly used traditional formulations included Decoction, Powder, and Decoction. The Chinese patent drugs included Pill, Injection, and Capsule. The most common paired medications were and , and . Two core combinations and one novel formula were discovered in the study. CONCLUSIONS Powder and Decoction are the basic formulations for syndrome of COVID-19. In addition, Decoction, Powder, Decoction and Decoction are the basic formulations for syndrome of COVID-19. The main medication characteristics are clearing heat, entilating lung, removing toxicity and removing turbidity. It shows that removing toxicity and eliminating evil are the prescription thought in treating epidemic disease of traditional Chinese medicine.
Betacoronavirus ; Cluster Analysis ; Coronavirus Infections ; therapy ; Data Mining ; Drugs, Chinese Herbal ; analysis ; therapeutic use ; Humans ; Medicine, Chinese Traditional ; statistics & numerical data ; Pandemics ; Pneumonia, Viral ; therapy

To analyze the medication regularity of traditional Chinese medicine(TCM) prescriptions for gastropyretic excessiveness diabetes recorded in Chinese Medicine Prescriptions Dictionary. A total of 103 eligible prescriptions were input into the system platform, and the Apriori algorithm was used to analyze their medication regularity. The 103 prescriptions for gastropyretic excessiveness diabetes were selected from the system, and 29 herb medicines were found with frequency of usage more than 8. Totally 33 commonly used herbal pairs(support degree≥10), twenty-three 3-herb core combinations(support degree≥8, confidence values≥0.5), and twenty-one 4-herb core combinations(confidence values≥0.5) were discovered after the medication regularity analysis by Apriori algorithm. The herbal medicine combinations with the highest correlation degree were discovered after the association rule analysis on the 103 prescriptions(support degree≥10, confidence values≥0.5). The four properties, five tastes, channel distributions and frequency of dose of the 103 prescriptions were also obtained after the corresponding analysis. According to the analysis and summary of the above data, the combination of Trichosanthis Radix, Anemarrhenae Rhizoma, Coptidis Rhizoma and Ophiopogonis Radix could reflect the medication regularity of TCM prescriptions for gastropyretic excessiveness diabetes to a certain degree, which is of great significance in guiding value in clinic.
Data Mining ; Diabetes Mellitus/drug therapy* ; Drug Prescriptions/statistics & numerical data* ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Medicine, Chinese Traditional

The present review summarizes the results of several follow-up studies assessing an asbestos product manufacturing plant in Chongqing, China, and discusses three controversial issues related to the carcinogenicity of asbestos. The first issue is the amphibole hypothesis, which asserts that the carcinogenicity of asbestos is limited to amphiboles, such as crocidolite, but not serpentines, such as chrysotile. However, considering the possible multiple component of asbestos carcinogenicity in the presence of tobacco smoke or other carcinogens, chrysotile cannot be regarded as non-carcinogenic. Additionally, in a practical sense, it is not possible to assume "pure" chrysotile due to its ubiquitous contamination with tremolite, which is a type of amphibole. Thus, as the International Agency for Research on Cancer (IARC) assessed, all forms of asbestos including chrysotile should be regarded carcinogenic to humans (Group 1). The second issue is the chrysotile/tremolite paradox, which is a phenomenon involving predominant levels of tremolite in the lung tissues of individuals who worked in locations with negligible levels of tremolite due to the exclusive use of chrysotile. Four possible mechanisms to explain this paradox have been proposed but this phenomenon does not support the claim that amphibole is inert. The final issue discussed is the textile mystery, i.e., the higher incidence of cancer in asbestos textile plants compared to asbestos mines where the same asbestos was produced and the exposure levels were comparable. This phenomenon was first reported in North America followed by UK and then in the present observations from China. Previously, levels of fiber exposure were calculated using a universal converting coefficient to estimate the mass concentration versus fiber concentration. However, parallel measurements of fiber and mass concentrations in the workplace and exposed air indicated that there are wide variations in the fiber/mass ratio, which unjustifies the universal conversion. It is possible that contamination by airborne non-fibrous particles in mines with mass fiber conversion led to the overestimation of fiber concentrations and resulted in the textile mystery. Although the use and manufacturing of asbestos has been banned in Japan, more than 10 million tons of asbestos had been imported and the majority remains in existing buildings. Thus, efforts to control asbestos exposure should be continued.
Asbestos ; classification ; toxicity ; Asbestos, Amphibole ; toxicity ; Asbestos, Serpentine ; toxicity ; Carcinogens ; China ; Follow-Up Studies ; Humans ; Lung Neoplasms ; chemically induced ; epidemiology ; Manufacturing and Industrial Facilities ; statistics & numerical data ; Mining ; statistics & numerical data ; Occupational Diseases ; epidemiology ; Occupational Exposure ; adverse effects ; Textiles ; Tobacco Smoking ; epidemiology

OBJECTIVES: Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. METHODS: We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. RESULTS: Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management. CONCLUSIONS: Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.
Artificial Intelligence ; Data Collection ; Data Mining* ; Decision Trees ; Delivery of Health Care ; Early Diagnosis ; Education ; Follow-Up Studies ; Fuzzy Logic ; Humans ; Information Systems* ; Mass Screening ; Methods ; Mobile Applications ; Patient Care ; Self Care ; Smartphone ; Statistics as Topic ; Support Vector Machine ; Telemedicine

The concept of big data, commonly characterized by volume, variety, velocity, and veracity, goes far beyond the data type and includes the aspects of data analysis, such as hypothesis-generating, rather than hypothesis-testing. Big data focuses on temporal stability of the association, rather than on causal relationship and underlying probability distribution assumptions are frequently not required. Medical big data as material to be analyzed has various features that are not only distinct from big data of other disciplines, but also distinct from traditional clinical epidemiology. Big data technology has many areas of application in healthcare, such as predictive modeling and clinical decision support, disease or safety surveillance, public health, and research. Big data analytics frequently exploits analytic methods developed in data mining, including classification, clustering, and regression. Medical big data analyses are complicated by many technical issues, such as missing values, curse of dimensionality, and bias control, and share the inherent limitations of observation study, namely the inability to test causality resulting from residual confounding and reverse causation. Recently, propensity score analysis and instrumental variable analysis have been introduced to overcome these limitations, and they have accomplished a great deal. Many challenges, such as the absence of evidence of practical benefits of big data, methodological issues including legal and ethical issues, and clinical integration and utility issues, must be overcome to realize the promise of medical big data as the fuel of a continuous learning healthcare system that will improve patient outcome and reduce waste in areas including nephrology.
Bias (Epidemiology) ; Classification ; Data Mining ; Decision Support Systems, Clinical ; Delivery of Health Care ; Epidemiology ; Ethics ; Humans ; Learning ; Nephrology ; Propensity Score ; Public Health Surveillance ; Statistics as Topic

INTRODUCTION72-hour emergency department (ED) reattendance is a widely-used quality indicator for quality of care and patient safety. It is generally assumed that patients who return within 72 hours of ED discharge (72-hour re-attendees) received inadequate treatment or evaluation. The current literature also suggests considerable variation in probable causes of 72-hour ED reattendances internationally. This study aimed to understand the characteristics of these patients at the ED of a Singapore tertiary hospital.

METHODSWe conducted a retrospective cohort study on all ED visits between 1 January 2013 and 31 December 2013. 72-hour re-attendees were compared against non-re-attendees based on patient demographics, mode of arrival, patient acuity category status (i.e. P1/P2/P3/P4), seniority ranking of doctor-in-charge and medical diagnoses. Multivariate analysis using the generalised linear model was conducted on variables associated with 72-hour ED re-attendance.

RESULTSAmong 104,751 unique patients, 3,065 (2.93%) were in the 72-hour re-attendees group. Multivariate analysis showed that the following risk factors were associated with higher risk of returning within 72 hours: male gender, older age, arrival by ambulance, triaged as P2, diagnoses of heart problems, abdominal pain or viral infection (all p < 0.001), and Chinese ethnicity (p = 0.006). There was no significant difference in the seniority ranking of the doctor-in-charge between both groups (p = 0.419).

CONCLUSIONSeveral patient and event factors were associated with higher risk of being a 72-hour re-attendee. This study forms the basis for hypothesis generation and further studies to explore reasons behind reattendances so that interventions can be developed to target high-risk groups.

Abdominal Pain ; Adult ; Aged ; Aged, 80 and over ; China ; Data Mining ; Electronic Health Records ; Emergency Medicine ; methods ; statistics & numerical data ; Emergency Service, Hospital ; statistics & numerical data ; Female ; Humans ; Male ; Middle Aged ; Multivariate Analysis ; Patient Discharge ; Patient Readmission ; Patient Safety ; Quality of Health Care ; Retrospective Studies ; Risk ; Singapore ; Tertiary Care Centers ; Triage ; methods ; Young Adult

This study was to investigate whether working in conditions of elevated concentrations of mine gases (CO2, CO, CH4, DMS) and dust may result in oxidative stress. Coal miners (n=94) from the Velenje Coal mine who were arranged into control group and three groups according to a number of consecutive working days. 8-isoprostane as a biological marker of oxidative stress was measured in exhaled breath condensate (EBC). Miners who worked for three consecutive days had higher 8-isoprostane values in EBC compared to the control group. Gas/dust concentrations and exposure time of a single/two day shift seem too low to trigger immediate oxidative stress.
Adult ; Biomarkers ; analysis ; Breath Tests ; Coal ; adverse effects ; Coal Mining ; manpower ; Dinoprost ; analogs & derivatives ; analysis ; Dust ; analysis ; Humans ; Male ; Middle Aged ; Miners ; statistics & numerical data ; Occupational Exposure ; analysis ; Oxidative Stress

PURPOSE: The purpose of this study is to analyze the differences in the perception of the "good doctor" image between faculty and students, based on the competencies of the "Korean doctor's role." METHODS: The study sample comprised 418 students and 49 faculty members in medical school. They were asked to draw images of a "good doctor," and the competencies were then analyzed using the Draw-A-Scientist test and the social network program Netminer 4.0. RESULTS: Of the competency areas, "communication and collaboration with patient" and "medical knowledge and clinical skills" were the most frequently expressed, and "education and research," "professionalism," and "social accountability" were less commonly expressed. Images of a good doctor by the faculty focused on competencies that were directly related to current clinical doctors. Conversely, those by the students expressed various competencies equally. CONCLUSION: We have provided basic data for faculties and schools to plan various education strategies to help students establish the image of a good doctor and develop the necessary competencies as physicians.
Clinical Competence/*standards ; Data Mining ; Faculty/*psychology ; Humans ; *Perception ; Physician's Role/*psychology ; Republic of Korea ; Schools, Medical ; Statistics as Topic ; Students, Medical/*psychology

Through computer-based technology and data mining method, with treatment in cases of bloodletting acupuncture therapy in collected literature as sample data, the association rule in data mining was applied. According to self-built database platform, the data was input, arranged and summarized, and eventually required data was acquired to perform the data mining of bleeding volume and method in blood-letting acupuncture therapy, which summarized its application rules and clinical values to provide better guide for clinical practice. There were 9 kinds of blood-letting tools in the literature, in which the frequency of three-edge needle was the highest, accounting for 84.4% (1239/1468). The bleeding volume was classified into six levels, in which less volume (less than 0.1 mL) had the highest frequency (401 times). According to the results of the data mining, blood-letting acupuncture therapy was widely applied in clinical practice of acupuncture, in which use of three-edge needle and less volume (less than 0.1 mL) of blood were the most common, however, there was no central tendency in general.
Acupuncture Points ; Acupuncture Therapy ; methods ; statistics & numerical data ; Bloodletting ; methods ; statistics & numerical data ; Data Mining ; Databases, Factual ; Humans

The aim of this study was to develop the data element standard directory of traditional Chinese medicine (TCM) clinical pharmacy information, to provide application standards and models of TCM clinical pharmacy for the electronic medical record (EMR). The developed line of work is as follows: initially establish research through four forms: literature analysis, questionnaires, discussion groups, expert advice. The research range from the Chinese herbal medicine research, herbal origin, harvesting, processing, identification of traits, physical and chemical identification, modern research, character, taste, Indications, clinical application, processing, dispensing medicine, Chinese medicine specifications, usage, dosage, caution, efficacy indications to small packaging applications, drug research, management and other related issues, including traditional Chinese medicine theory, application and hospital management information; according to the general and part 16 content of the national "Health Information Data Element Standards", and the basic method of extracting data element to study and develop the data element of TCM clinical pharmacy information from the defining content. Correspondingly propose the ideas and methods of construction of the "Data Element Standard Directory of TCM Clinical Pharmacy Information", sort out medicine clinical information data element standard catalog, divided into basic categories, clinical application class, management class three parts, and set norms and standards of identifying data elements, definitions, allowable value of traditional Chinese medicine clinical information, and discuss the sources and standards of information collection, leaving the interface, standardized and scientific terminology, docking with the existing standards, maintenance and management program and oter issues.
China ; Data Mining ; methods ; statistics & numerical data ; Database Management Systems ; standards ; statistics & numerical data ; Electronic Health Records ; standards ; statistics & numerical data ; Evidence-Based Medicine ; methods ; statistics & numerical data ; Humans ; Information Dissemination ; methods ; Medicine, Chinese Traditional ; methods ; Phytotherapy ; methods ; statistics & numerical data