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简介：AbstractObjective:Medical data mining and sharing is an important process in E-Health applications. However, because these data consist of a large amount of personal private information of patients, there is the risk of privacy disclosure when sharing and mining. Therefore, ensuring the security of medical big data in the process of publishing, sharing, and mining has become the focus of current research. The objective of our study is to design a framework based on a differential privacy protection mechanism to ensure the secure sharing of medical data. We developed a privacy protection query language (PQL) that integrates multiple data mining methods and provides a secure sharing function.Methods:This study is mainly performed in Xuzhou Medical University, China and designs three sub-modules: a parsing module, mining module, and noising module. Each module encapsulates different computing methods, such as a composite parser and a noise theory. In the PQL framework, we apply the differential privacy theory to the results of the computing between modules to guarantee the security of various mining algorithms. These computing devices operate independently, but the mining results depend on their cooperation. In addition, PQL is encapsulated in MNSSp3 that is a data mining and security sharing platform and the data comes from public data sets, such as UCBI. The public data set (NCBI database) was used as the experimental data, and the data collection time was January 2020.Results:We designed and developed a query language that provides functions for medical data mining, sharing, and privacy preservation. We theoretically proved the performance of the PQL framework. The experimental results show that the PQL framework can ensure the security of each mining result and the availability of the output results is above 97%.Conclusion:Our framework enables medical data providers to securely share health data or treatment data and develops a usable query language, based on a differential privacy mechanism, that enables researchers to mine information securely using data mining algorithms.

简介：AbstractFollowing the emergence of COVID-19 outbreak, numbers of studies have been conducted to curtail the global spread of the virus by identifying epidemiological changes of the disease through developing statistical models, estimation of the basic reproduction number, displaying the daily reports of confirmed and deaths cases, which are closely related to the present study. Reliable and comprehensive estimation method of the epidemiological data is required to understand the actual situation of fatalities caused by the epidemic. Case fatality rate (CFR) is one of the cardinal epidemiological parameters that adequately explains epidemiology of the outbreak of a disease. In the present study, we employed two statistical regression models such as the linear and polynomial models in order to estimate the CFR, based on the early phase of COVID-19 outbreak in Nigeria (44 days since first reported COVID-19 death). The estimate of the CFR was determined based on cumulative number of confirmed cases and deaths reported from 23 March to 30 April, 2020. The results from the linear model estimated that the CFR was 3.11% (95% CI: 2.59% - 3.80%) with R2 value of 90% and p-value of < 0.0001. The findings from the polynomial model suggest that the CFR associated with the Nigerian outbreak is 3.0% and may range from 2.23% to 3.42% with R2 value of 93% and p-value of <0.0001. Therefore, the polynomial regression model with the higher R2 value fits the dataset well and provides better estimate of CFR for the reported COVID-19 cases in Nigeria.

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简介：AbstractBackground:Despite almost two decades of well-funded and comprehensive response efforts by the Chinese Government, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) remains a major problem in China. Yet, few studies have recently examined long-term trends in HIV/AIDS prevalence, incidence, and mortality at the national level. This study aimed to determine the prevalence, incidence, and mortality trends for HIV/AIDS over the past 28 years in China.Methods:We conducted a descriptive, epidemiological, secondary analysis of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 data. To evaluate trends in prevalence, incidence, and mortality over the study period from 1990 to 2017, we calculated values for annual percentage change (APC) and corresponding 95% confidence intervals (CIs) using joinpoint regression analysis.Results:A significant increase in HIV/AIDS prevalence was observed for 1990 to 2009 (APC: 10.7; 95% CI: 10.4, 11.0; P < 0.001), and then remained stable for 2009 to 2017 (APC: 0.7; 95% CI: -0.3, 1.7; P = 0.1). A significant increase in HIV incidence was also observed for 1990 to 2005 (APC: 13.0; 95% CI: 12.6, 13.4; P < 0.001), and then a significant decrease was detected for 2005 to 2017 (APC: -6.5; 95% CI: -7.0, -6.1; P < 0.001). A significant increase in AIDS-related mortality rate was detected for 1990 to 2004 (APC: 10.3; 95% CI: 9.3, 11.3; P < 0.001), followed by a period of stability for 2004 to 2013 (APC: 1.3; 95% CI: -0.7, 3.3; P = 0.2), and then another significant increase for 2013 to 2017 (APC: 15.3; 95% CI: 8.7, 22.2; P < 0.001).Conclusions:Although prevalence has stabilized and incidence has declined, AIDS-related mortality has risen sharply in recent years. These findings suggest more must be done to bring people into treatment earlier, retain them in treatment more effectively, actively seek to reenter them in treatment if they dropout, and improve the quality of treatment and care regimens.

简介：AbstractPurpose:The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation.Methods:The study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014-2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients' prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria.Results:Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions.Conclusion:Data mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.