Big Data Analytics in Medicine and Healthcare

Abstract

This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various - omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

Brief introduction of medical database and data mining technology in big data era

“…The medical industry is producing a large number of data every day, which is an important area of big data's application. In order to provide patients with the best services and care, medical institutions in many countries have proposed various modes of medical information systems 7 . Therefore, how to better develop and utilize the huge medical big data has become the focus of attention, and promoting the research and application of medical big data has become a key factor in modern medical research.…”

“…modes of medical information systems. 7 Therefore, how to better develop and utilize the huge medical big data has become the focus of attention, and promoting the research and application of medical big data has become a key factor in modern medical research.…”

Orthodontics in the era of big data analytics

“…According to the National Institute of Health (NIH), data science is an “ interdisciplinary field of enquiry in which quantitative and analytical approaches, processes, and systems are developed and used to extract knowledge and insights from increasingly large and/or complex sets of data .” The terms big science, big data and big data analytics have been used interchangeably. It has come to be accepted that big data should comprise six fundamental qualities: volume, veracity, variety, variability, velocity and value . The advent of multiple data sources in the healthcare field such as electronic health records, omics data, imaging, biometrics, environmental data and patient collected information has armed us with an empirical data‐driven framework to make clinical decisions .…”

“…Yet, not all healthcare facilities and providers are equipped with the necessary resources in terms of trained personnel, core computing or the fundamental knowledge to leverage the advantages of the big data platform. Specialties such as oncology, cardiology, surgery and radiology have been at the forefront of adapting to the big data environment and have made massive strides in using some of the big data analytical methods and data sources to deliver personalized and precision care . Using different data sources and new age big data analytic tools, these specialties have challenged the long‐held traditional monolithic view of disease .…”

“…Big data analytic tools have been successful in identifying personalized disease concepts and subtypes and delivering targeted aetiology‐based therapies . This is reflected in the large number of peer‐reviewed publications stemming from these fields documenting the individualized nature of care delivered …”