Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub- Groups of Patients Using Big Data Solutions
The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.
Data Mining Electronic Health Records Population Health
Michael A.Phinney Yan Zhuang Sean Lander Lincoln Sheets Jerry C.Parker Chi-Ren Shyu
Department of Electrical Engineering and Computer Science,University of Missouri,Columbia,Missouri,U Informatics Institute,University of Missouri,Columbia,Missouri,USA Informatics Institute,University of Missouri,Columbia,Missouri,USA;School of Medicine,University of School of Medicine,University of Missouri,Columbia,Missouri,USA Department of Electrical Engineering and Computer Science,University of Missouri,Columbia,Missouri,U
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
544-548
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）