Big data in medicine and health

Data are flooding in at rates never seen before, doubling every 18 months, as a result of greater access to customer data from public, proprietary and purchased sources, as well as new information gathered from Web communities and newly deployed smart assets. These trends are broadly known as ‘big data’, and new software, technology and tools are needed to cope with the high volume, variety, and velocity of the big data. Big data often refers to data available from various domains such as from business, Web traffic and social networks to software and sensors that monitor shipments, suppliers and customers, and big data research encourages domain specific expertise or cross-disciplinary collaboration.

Due to the recent development or maturation of database, data storage, data capturing, patient monitoring and sensor technologies, huge medical and health data have been generated at hospitals and medical organizations at unprecedented speed. Those data are a very valuable resource for improving health delivery, health care and decision making and better risk analysis and diagnosis.

For its new thematic series on ‘Big data in medicine and health’, to address the research issues and challenges in medical big data, Health Information Science and Systems is calling for papers related to big data and innovative applications in the medical and health domain. This thematic series will provide an opportunity for scholars and researchers in big data and health science to share and exchange their recent knowledge and experiences in medical and health information systems, data mining and data management, health care and health delivery.

The topics include but are not limited to the following:

  • • Medical data capturing, data integration, data mining and data analysis
  • • Use of big data to drive better health delivery
  • • Application of data analytics to big data to improve health care performance
  • • Novel technologies for handling or analyzing big data in health
  • • Novel applications with medical data



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