A visual analysis approach to cohort study of electronic patient records

Author

Chun-Fu Wang ; Jianping Li ; Kwan-Liu Ma ; Chih-Wei Huang ; Yu-Chuan Li

Author_Institution

Dept. of Comput. Sci., Univ. of California at Davis, Davis, CA, USA

fYear

2014

fDate

2-5 Nov. 2014

Firstpage

521

Lastpage

528

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.

Keywords

data analysis; data mining; data structures; diseases; electronic health records; kidney; EMR accessibility; EMR complexity; EMR growing size; chronic kidney disease patients; cohort study; current EMR systems; disease; electronic medical records; electronic patient records; hidden data structures; knowledge extraction; medical policy makers; multidimensional visual aggregation; physicians; physicians, clinical researchers; visual analysis approach; visual mining solution; Aggregates; Complexity theory; Data visualization; Diseases; Drugs; Trajectory; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on

Conference_Location

Belfast

Type

conf

DOI

10.1109/BIBM.2014.6999214

Filename

6999214