Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Investigating the relationship between religious coping, appraisals, social support, and symptoms of Posttraumatic Stress Disorder (PTSD): A correlational study using an Islamic community sample.

Background Contemporary models of PTSD view posttraumatic appraisals and social support as important factors in the onset and maintenance of this condition (e.g., Ehlers & Clark, 2000). Islam is central to the lives of its adherents (e.g., Hamdan, 2007) and religion influences its followers’ beliefs and coping with adversity (Pargament, 1997). The impact of religious beliefs on coping with psychological distress has received increasing attention in the last two decades (Braam et al., 2010). However, like the literature on PTSD (e.g., Foa et al., 2009), this research has almost exclusively focused on Christian, Western populations (e.g., Abu-Raiya & Pargament, 2014). Therefore, this study aimed to better understand how religious coping, appraisals (religious and non-religious), and perceived social support influence the posttraumatic adjustment of Muslim trauma survivors. Method A cross-sectional, correlational design was conducted to study the relationships between PTSD symptoms and posttraumatic appraisals, negative religious coping, negative Islamic appraisals, and perceived social support. Eighty-eight Arabic-speaking Muslim trauma survivors, recruited from the community, completed a questionnaire booklet measuring the study variables. Results Contrary to expectations, symptoms of PTSD were not significantly associated with negative religious coping, negative Islamic appraisals, and perceived social support. However, posttraumatic appraisals were associated with, and predictive of, PTSD Doctoral Thesis: Investigating the relationship between Azi Berzengi religious coping, appraisals, social support, and symptoms of Posttraumatic Stress Disorder (PTSD): A correlational study using an Islamic community sample iii symptoms. Exploratory mediation analyses revealed that posttraumatic appraisals also mediated the relationships between negative religious coping and PTSD symptoms, and between negative Islamic appraisals and PTSD symptoms. Discussion The current theoretical and clinical emphasis on posttraumatic cognitive appraisals in PTSD may also be applicable to Muslim trauma survivors. Contrary to previous research, however, negative religious coping and negative Islamic appraisals appear to have an indirect effect on PTSD symptoms. Several methodological limitations, including the heterogeneous sample composition, could account for some of the findings. These limitations, alongside the theoretical and clinical implications of the results, are discussed

Data Mining Techniques for Complex User-Generated Data

Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains