| Autor: |
Phinney MA; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA., Zhuang Y; Informatics Institute, University of Missouri, Columbia, Missouri, USA., Lander S; Informatics Institute, University of Missouri, Columbia, Missouri, USA., Sheets L; Informatics Institute, University of Missouri, Columbia, Missouri, USA., Parker JC; School of Medicine, University of Missouri, Columbia, Missouri, USA., Shyu CR; Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA. |
| Abstrakt: |
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. |
