Abstract

The ability to analyze and assimilate Electronic Medical Records (EMR) has great value to physicians, clinical researchers, and medical policy makers. Current EMR systems do not provide adequate support for fully exploiting the data. The growing size, complexity, and accessibility of EMRs demand a new set of tools for extracting knowledge of interest from the data. This paper presents an interactive visual mining solution for cohort study of EMRs. The basis of our design is multidimensional, visual aggregation of the EMRs. The resulting visualizations can help uncover hidden structures in the data, compare different patient groups, determine critical factors to a particular disease, and help direct further analyses. We introduce and demonstrate our design with case studies using EMRs of 14,567 Chronic Kidney Disease (CKD) patients.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages521-528
Number of pages8
ISBN (Print)9781479956692
DOIs
Publication statusPublished - Dec 29 2014
Event2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 - Belfast, United Kingdom
Duration: Nov 2 2014Nov 5 2014

Other

Other2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
CountryUnited Kingdom
CityBelfast
Period11/2/1411/5/14

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Wang, C. F., Li, J., Ma, K. L., Huang, C. W., & Li, Y. C. (2014). A visual analysis approach to cohort study of electronic patient records. In Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 (pp. 521-528). [6999214] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2014.6999214