Visual Analytics to Optimize Patient-Population Evidence Delivery for Personalized Care

Authors:
Ketan K. Mane;Phillips Owen;Charles Schmitt;Kirk Wilhelmsen;Kenneth Gersing;Ricardo Pietrobon;Igor Akushevich
Affiliations:
RENCI, UNC-Chapel Hill, 100 Europa Dr, Suite 540, Chapel Hill, NC, +1-919-445-9703;RENCI, UNC-Chapel Hill, 100 Europa Dr, Suite 540, Chapel Hill, NC, +1-919-445-9703;RENCI, UNC-Chapel Hill, 100 Europa Dr, Suite 540, Chapel Hill, NC, +1-919-445-9703;RENCI, UNC-Chapel Hill, 100 Europa Dr, Suite 540, Chapel Hill, NC, +1-919-445-9703;Duke University, Duke University Medical Center, Durham, NC, +1-919-684-0103;Duke University, Duke University Medical Center, Durham, NC, +1-919-684-0103;Duke University, Duke University Medical Center, Durham, NC, +1-919-684-0103
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

Electronic medical records (EMR) can be used to identify cohorts of patients who are clinically comparable to an individual patient. In this paper, we describe an approach that applies visual analytics to EMR data to describe the clinical course for an individual patient, display outcomes for a comparable cohort stratified by treatment, and generate predictions regarding a patient's clinical course based on treatment options. The visual display of information is designed to help clinicians choose among alternative therapies based on the EMR-derived outcomes of the cohort.