Postmortem diagnosis of severe factor X deficiency in a fetus with intracranial hemorrhage resulting in intrauterine death.

In patients with severe congenital factor X deficiency, spontaneous intracranial hemorrhage (ICH) is particularly frequent in early childhood. We describe a case of fetal death at 26 weeks due to massive ICH. Gene panel analysis of postmortem samples revealed homozygosity for a pathologic F10 gene variant (c.1210T>C, p.Cys404Arg), which impedes correct folding of the catalytic serine protease domain and, therefore, causes a significant reduction in FX levels. The parents, not consanguineous but of the same ethnic community, were found to be heterozygous for this variant and did not have any personal or family history of abnormal bleeding. To the best of our knowledge, this is the first reported case of severe FX deficiency resulting in ICH diagnosed through postmortem genetic analysis. It illustrates the importance of exploring the etiology of fetal or neonatal ICH, which may impact future pregnancies, and the treatment of a potential coagulopathy in the child.

Pandemic Analytics: How Countries are Leveraging Big Data Analytics and Artificial Intelligence to Fight COVID-19?

Emergence of coronavirus in December 2019 and its spread across the world in the following months has made it a global health concern. The uncertainty about its evolution, transmission and effect of SARS-CoV-2, has left the countries and their governments in a worrisome state. Ambiguity about the strategies that would work towards mitigating the impact of virus has prompted them to use data-driven methods. Several countries started applying big data and advanced analytics technology for management of the crisis. This study aims to understand how different nations have employed analytics to deal with COVID-19. This paper reviews various strategies employed by different governments and organizations across nations that use advanced analytics to tackle pandemic. In the current emergency of corona virus, there have been several measures that organizations have taken to mitigate its impact, thanks to the evolution of computing technology. Big data and analytical tools provide various solutions like detection of existing COVID-19 cases, prediction of future outbreak, anticipation of potential preventive and therapeutic agents, and assistance in informed decision-making. This review discusses the big data analytics and artificial intelligence approaches that policy makers, researchers, epidemiologists and private organizations have adopted. By examining the different ways and areas where data analytics has been utilized, this study provides the other nations with the progressive scheme to address the pandemic.

Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study.

The domain of healthcare has always been flooded with a huge amount of complex data, coming in at a very fast-pace. A vast amount of data is generated in different sectors of healthcare industry: data from hospitals and healthcare providers, medical insurance, medical equipment, life sciences and medical research. With the advancement in technology, there is a huge potential for utilization of this data for transforming healthcare. The application of analytics, machine learning and artificial intelligence over big data enables identification of patterns and correlations and hence provides actionable insights for improving the delivery of healthcare. There have been many contributions to the literature in this topic, but we lack a comprehensive view of the current state of research and application. This paper focuses on assessing the available literature in order to provide the researchers with evidence that enable fostering further development in this area. A systematic mapping study was conducted to identify and analyze research on big data analytics and artificial intelligence in healthcare, in which 2421 articles between 2013 and February 2019 were evaluated. The results of this study will help understand the needs in application of these technologies in healthcare by identifying the areas that require additional research. It will hence provide the researchers and industry experts with a base for future work.

Concurrence of big data analytics and healthcare: A systematic review.

BACKGROUND: The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. PURPOSE: This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. DATA SOURCES: A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. STUDY SELECTION: Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. DATA EXTRACTION: Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. RESULTS: A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. CONCLUSION: This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.