Big Data Application in Biomedical Research and Health Care: A Literature Review

Abstract

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.

Big Data and Public Health Systems: Issues and Opportunities

“…In addition, the inclusion of geographical and environmental information may further increase the ability to interpret gathered data and extract new knowledge [11] [12].…”

Big Data in Bioinformatics

“…При этом применение технологий больших данных в биоинформатике, биомедицине и здравоохранении [66] способно не просто улучшить, а кардинально, революционно изменить ситуацию в этой области. Однако, несмотря на некоторые успехи в развитии методов анализа и в практическом применении новых технологий работы с большими данными, в биоинформатике и биомедицине имеется огромный неиспользованный потенциал для их развития.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…ML has provided significant benefits to a range of fields, including artificial intelligence, computer vision, speech recognition, and natural language processing, allowing researchers and developers to extract vital information from data, provide personalised experiences, and develop intelligent systems [2]. Within health fields such as bioinformatics, ML has led to significant advances by enabling speedy and scalable analysis of complex data [3]. Such analytic techniques are also being explored with mental health data, with the broad potential of both improving patient outcomes and enhancing understanding of psychological conditions and their management within the wider community.…”

“…Two reviews have been completed on this topic to date; yet neither review systematically assessed all published research using ML in mental health applications. First, Luo et al [3] investigated big data applications in the field of biomedical research and health care, finding many novel applications in bioinformatics, clinical informatics, imaging informatics, and public health informatics. However examples and opportunities for ML in the mental health context were only briefly discussed (specifically detecting depression using social media and predictive models for classifying psychological conditions), due to the broader aim of this study beyond mental health.…”