Hossam M. Hammady

Bio: Hossam M. Hammady is an academic researcher from Qatar Computing Research Institute. The author has contributed to research in topics: Analytics & Data processing system. The author has an hindex of 6, co-authored 8 publications receiving 3062 citations.

Rayyan-a web and mobile app for systematic reviews.

Abstract: Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan ( http://rayyan.qcri.org ), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.

VERA: A Platform for Veracity Estimation over Web Data

Abstract: Social networks and the Web in general are characterized by multiple information sources often claiming conflicting data values. Data veracity is hard to estimate, especially when there is no prior knowledge about the sources or the claims and in time-dependent scenarios where initially very few observers can report first information. Despite the wide set of recently proposed truth discovery approaches, "no-one-fits-all" solution emerges for estimating the veracity of on-line information in open contexts. However, analyzing the space of conflicting information and disagreeing sources might be relevant, as well as ensembling multiple truth discovery methods. This demonstration presents VERA, a Web-based platform that supports information extraction from Web textual data and micro-texts from Twitter and estimates data veracity. Given a user query, VERA systematically extracts entities and relations from Web content, structures them as claims relevant to the query and gathers more conflicting/corroborating information. VERA combines multiple truth discovery algorithms through ensembling returns the veracity label and score of each data value and the trustworthiness scores of the sources. VERA will be demonstrated through several real-world scenarios to show its potential value for fact-checking from Web data.

Learning to identify relevant studies for systematic reviews using random forest and external information

Abstract: We tackle the problem of automatically filtering studies while preparing Systematic Reviews (SRs) which normally entails manually inspecting thousands of studies to identify the few to be included. The problem is modeled as an imbalanced data classification task where the cost of misclassifying the minority class is higher than the cost of misclassifying the majority class. This work introduces a novel method for representing systematic reviews based not only on lexical features, but also utilizing word clustering and citation features. This novel representation is shown to outperform previously used features in representing systematic reviews, regardless of the classifier. Our work utilizes a random forest classifier with the novel features to accurately predict included studies with high recall. The parameters of the random forest are automatically configured using heuristics methods thus allowing us to provide a product that is usable in real scenarios. Experiments on a dataset containing 15 systematic reviews that were prepared by health care professionals show that our approach can achieve high recall while helping the SR author save time.

Rheem: Enabling Multi-Platform Task Execution

Abstract: Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.

AllegatorTrack: Combining and reporting results of truth discovery from multi-source data

Abstract: In the Web, a massive amount of user-generated contents is available through various channels, e.g., texts, tweets, Web tables, databases, multimedia-sharing platforms, etc. Conflicting information, rumors, erroneous and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. How do you figure out that a lie has been told often enough that it is now considered to be true? How many lying sources are required to introduce confusion in what you knew before to be the truth? To answer these questions, we present AllegatorTrack, a system that discovers true claims among conflicting data from multiple sources.

Interventions for preventing obesity in children

Abstract: The current evidence suggests that many diet and exercise interventions to prevent obesity in children are not effective in preventing weight gain, but can be effective in promoting a healthy diet and increased physical activity levels.Being very overweight (obese) can cause health, psychological and social problems for children. Children who are obese are more likely to have weight and health problems as adults. Programmes designed to prevent obesity focus on modifying one or more of the factors considered to promote obesity.This review included 22 studies that tested a variety of intervention programmes, which involved increased physical activity and dietary changes, singly or in combination. Participants were under 18 and living in Asia, South America, Europe or North America. There is not enough evidence from trials to prove that any one particular programme can prevent obesity in children, although comprehensive strategies to address dietary and physical activity change, together with psycho-social support and environmental change may help. There was a trend for newer interventions to involve their respective communities and to include evaluations.Future research might usefully assess changes made on behalf of entire populations, such as improvements in the types of foods available at schools and in the availability of safe places to run and play, and should assess health effects and costs over several years.The programmes in this review used different strategies to prevent obesity so direct comparisons were difficult. Also, the duration of the studies ranged from 12 weeks to three years, but most lasted less than a year.

The prevalence of depression, anxiety, and sleep disturbances in COVID-19 patients: a meta-analysis

Abstract: Evidence from previous coronavirus outbreaks has shown that infected patients are at risk for developing psychiatric and mental health disorders, such as depression, anxiety, and sleep disturbances. To construct a comprehensive picture of the mental health status in COVID-19 patients, we conducted a systematic review and random-effects meta-analysis to assess the prevalence of depression, anxiety, and sleep disturbances in this population. We searched MEDLINE, EMBASE, PubMed, Web of Science, CINAHL, Wanfang Data, Wangfang Med Online, CNKI, and CQVIP for relevant articles, and we included 31 studies (n = 5153) in our analyses. We found that the pooled prevalence of depression was 45% (95% CI: 37-54%, I2 = 96%), the pooled prevalence of anxiety was 47% (95% CI: 37-57%, I2 = 97%), and the pooled prevalence of sleeping disturbances was 34% (95% CI: 19-50%, I2 = 98%). We did not find any significant differences in the prevalence estimates between different genders; however, the depression and anxiety prevalence estimates varied based on different screening tools. More observational studies assessing the mental wellness of COVID-19 outpatients and COVID-19 patients from countries other than China are needed to further examine the psychological implications of COVID-19 infections.

Mental health outcomes of quarantine and isolation for infection prevention: a systematic umbrella review of the global evidence.

Abstract: Objectives Transmission of infectious diseases is often prevented by quarantine and isolation of the populations at risk. These approaches restrict the mobility, social interactions, and daily activities of the affected individuals. In recent coronavirus disease 2019 (COVID-19) pandemic, quarantine and isolation are being adopted in many contexts, which necessitates an evaluation of global evidence on how such measures impact the mental health outcomes among populations. This umbrella review aimed to synthesize the available evidence on mental health outcomes of quarantine and isolation for preventing infectious diseases. Methods We searched nine major databases and additional sources and included articles if they were systematically conducted reviews, published as peer-reviewed journal articles, and reported mental health outcomes of quarantine or isolation in any population. Results Among 1,364 citations, only eight reviews met our criteria. Most of the primary studies in those reviews were conducted in high-income nations and in hospital settings. These articles reported a high burden of mental health problems among patients, informal caregivers, and healthcare providers who experienced quarantine or isolation. Prevalent mental health problems among the affected individuals include depression, anxiety, mood disorders, psychological distress, posttraumatic stress disorder, insomnia, fear, stigmatization, low self-esteem, lack of self-control, and other adverse mental health outcomes. Conclusions This umbrella review found severe mental health problems among individuals and populations who have undergone quarantine and isolation in different contexts. This evidence necessitates multipronged interventions including policy measures for strengthening mental health services globally and promoting psychosocial wellbeing among high-risk populations.