A visual analysis approach to cohort study of electronic patient records
Chunfu Wang, Jianping Kelvin Li, Kwan‐Liu Ma, Chih‐Wei Huang, Yu‐Chuan Li · 2014
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.