Unsupervised text Feature Selection using memetic Dichotomous Differential Evolution

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection

Dietary factors and Type 2 diabetes in the Middle East : what is the evidence for an association? - A systematic review.

This review aims to search and summarise the available evidence on the association between dietary factors and type 2 diabetes mellitus (T2DM) in Middle Eastern populations, where diabetes prevalence is among the highest in the world. Electronic databases were searched; authors, libraries, and research centres in the Middle East were contacted for further studies and unpublished literature. Included studies assessed potential dietary factors for T2DM in Middle Eastern adults. Two reviewers assessed studies independently. Extensive searching yielded 17 studies which met the inclusion criteria for this review. The findings showed that whole-grain intake reduces the risk of T2DM, and potato consumption was positively correlated with T2DM. Vegetables and vegetable oil may play a protective role against T2DM. Dietary patterns that are associated with diabetes were identified, such as Fast Food and Refined Grains patterns. Two studies demonstrated that lifestyle interventions decreased the risk of T2DM. In summary, the identified studies support an association between some dietary factors and T2DM; however, many of the included studies were of poor methodological quality so the findings should be interpreted with caution. The review draws attention to major gaps in current evidence and the need for well-designed studies in this area

Efficacy of hepatic transplantation in patients with primary sclerosing cholangitis

Controlled trials to assess the therapeutic benefit of orthotopic hepatic transplantation (OHTx) for primary sclerosing cholangitis (PSC) cannot be justified in view of improvement of patient survival after this operation since 1981. However, the actual patient survival with OHTx can be compared with the Mayo model estimated survival probabilities without OHTx. This model, which encompasses physical, biochemical and histopathologic parameters of PSC, was constructed from a study of 392 conservatively treated PSC patients at five international centers in England and North America. We compared the actual survival of 216 adult patients with the diagnosis of advanced PSC who underwent hepatic replacement with the expected survival estimated by the Mayo PSC natural history model, 'the simulated control technique.' OHTx was performed at the University of Pittsburgh and Mayo Medical Center between 5 December 1981 and 26 December 1990. The mean (plus or minus standard deviation) post-OHTx follow-up period was 34 ± 25 months (range of zero to 104 months). Before transplantation, biliary or portal hypertensive operation, or both, was performed upon 104 patients. At operation, the mean age of recipients was 42.1 ± 11.3 years and the mean value of total serum bilirubin was 13.3 ± 13.0 milligrams per deciliter. Extensive septal fibrosis and cirrhosis were histologically documented in 97 percent of the patients, with splenomegaly in 63 percent. Immunosuppressive therapy was based primarily on cyclosporin in 184 recipients and FK-506 in 32. Within six months, the Kaplan-Meier survival probability after OHTx (0.89) already was higher than predicted by the Mayo model (0.83). At five years, the Kaplan-Meier actual survival with OHTx was 0.73 compared with 0.28 expected Mayo model survival. The overall increased survival rate with transplantation was statistically significant (chi-square equals 126.6; p<0.001). At all risk stratifications, OHTx significantly improved survival with a p value of 0.031 (low risk), 0.001 (moderate risk) and <0.001 (high risk). Thus, OHTx is effective therapy for PSC. Disease gravity and unsuspected cholangiocarcinoma in the excised native liver adversely influenced short and long term survival rates after transplantation, respectively

Arab female and male perceptions of factors facilitating and inhibiting their physical activity: Findings from a qualitative study in the Middle East

Objectives: Physical inactivity is a leading global risk to health by contributing to obesity and other chronic diseases. Many chronic non-communicable diseases, such as cancer, diabetes, and cardiovascular diseases (CVDs), can be prevented and controlled by modifying lifestyle behaviors such as physical activity [PA]. However, prevalence of insufficient physical activity and obesity is high in the Middle East Region. In Qatar, the incidence rates of CVDs, diabetes, colon, and breast cancer have been rising rapidly. The purpose of this study was to explore facilitators and barriers influencing PA of adult Arab men and women living in Qatar and to understand what they think would be helpful to increase PA. The goal of the research is to identify culturally appropriate and effective interventions that improve the health of Arab population. Design: Using the socioecological model as the theoretical framework, we conducted an exploratory qualitative study with 128 Arab adult men and women living in Qatar. We utilized focus group interviews to collect the data and performed thematic analysis to generate themes. Results: At the individual level, perceived benefits of PA, presence of diseases, person’s will, motivation and goals, and time to exercise influenced the individual’s PA. At the sociocultural level, religious teachings of Islam, cultural, attitude, beliefs, and practices, and informal support influenced the participants’ PA. At the organizational and political level, physical environment to exercise, accessibility of facilities, organizational support, and health information about PA influenced their PA. Conclusion: Arab men and women are aware of the importance and benefits of PA. They have the motivation to be physically active, but in the absence of supportive environment, their knowledge might not translate into action. Creating supportive environments at multiple levels that are conducive to PA is warranted

Differentially private multidimensional data publishing

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Various organizations collect data about individuals for various reasons, such as service improvement. In order to mine the collected data for useful information, data publishing has become a common practice among those organizations and data analysts, research institutes, or simply the general public. The quality of published data significantly affects the accuracy of the data analysis and thus affects decision making at the corporate level. In this study, we explore the research area of privacy-preserving data publishing, i.e., publishing high-quality data without compromising the privacy of the individuals whose data are being published. Syntactic privacy models, such as k-anonymity, impose syntactic privacy requirements and make certain assumptions about an adversary’s background knowledge. To address this shortcoming, we adopt differential privacy, a rigorous privacy model that is independent of any adversary’s knowledge and insensitive to the underlying data. The published data should preserve individuals’ privacy, yet remain useful for analysis. To maintain data utility, we propose DiffMulti, a workload-aware and differentially private algorithm that employs multidimensional generalization. We devise an efficient implementation to the proposed algorithm and use a real-life data set for experimental analysis. We evaluate the performance of our method in terms of data utility, efficiency, and scalability. When compared to closely related existing methods, DiffMulti significantly improved data utility, in some cases, by orders of magnitude

Differentially Private Release of Heterogeneous Network for Managing Healthcare Data

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet , an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets

SafePath: Differentially-private publishing of passenger trajectories in transportation systems

© 2018 Elsevier B.V. In recent years, the collection of spatio-temporal data that captures human movements has increased tremendously due to the advancements in hardware and software systems capable of collecting person-specific data. The bulk of the data collected by these systems has numerous applications, or it can simply be used for general data analysis. Therefore, publishing such big data is greatly beneficial for data recipients. However, in its raw form, the collected data contains sensitive information pertaining to the individuals from which it was collected and must be anonymized before publication. In this paper, we study the problem of privacy-preserving passenger trajectories publishing and propose a solution under the rigorous differential privacy model. Unlike sequential data, which describes sequentiality between data items, handling spatio-temporal data is a challenging task due to the fact that introducing a temporal dimension results in extreme sparseness. Our proposed solution introduces an efficient algorithm, called SafePath, that models trajectories as a noisy prefix tree and publishes ϵ-differentially-private trajectories while minimizing the impact on data utility. Experimental evaluation on real-life transit data in Montreal suggests that SafePath significantly improves efficiency and scalability with respect to large and sparse datasets, while achieving comparable results to existing solutions in terms of the utility of the sanitized data

Seek, and ye shall find: Accessing the global epidemiological literature in different languages.

The thematic series Beyond English: Accessing the global epidemiological literature in Emerging Themes in Epidemiology highlights the wealth of epidemiological and public health literature in the major languages of the world, and the bibliographic databases through which they can be searched and accessed. This editorial suggests that all systematic reviews in epidemiology and public health should include literature published in the major languages of the world and that the use of regional and non-English bibliographic databases should become routine.Published versio

COVID-19 Pandemic Development in Jordan-Short-Term and Long-Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves' occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.Peer reviewe