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J Am Med Inform Assoc. 2015 Mar;22(2):324-9. doi: 10.1136/amiajnl-2014-002965. Epub 2014 Oct 21.

Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization.

Author information

1
Division of Hematology/Oncology, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
2
Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA Division of General Internal Medicine, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA.
3
Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
4
Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA Children's Hospital Informatics Program at Harvard-MIT Division of Health Science, Boston, Massachusetts, USA Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Abstract

Our aim was to uncover unrecognized phenomic relationships using force-based network visualization methods, based on observed electronic medical record data. A primary phenotype was defined from actual patient profiles in the Multiparameter Intelligent Monitoring in Intensive Care II database. Network visualizations depicting primary relationships were compared to those incorporating secondary adjacencies. Interactivity was enabled through a phenotype visualization software concept: the Phenomics Advisor. Subendocardial infarction with cardiac arrest was demonstrated as a sample phenotype; there were 332 primarily adjacent diagnoses, with 5423 relationships. Primary network visualization suggested a treatment-related complication phenotype and several rare diagnoses; re-clustering by secondary relationships revealed an emergent cluster of smokers with the metabolic syndrome. Network visualization reveals phenotypic patterns that may have remained occult in pairwise correlation analysis. Visualization of complex data, potentially offered as point-of-care tools on mobile devices, may allow clinicians and researchers to quickly generate hypotheses and gain deeper understanding of patient subpopulations.

KEYWORDS:

Data display; Data mining; Decision making; computer-assisted; Medical informatics applications; Phenotype

PMID:
25336590
PMCID:
PMC6080728
DOI:
10.1136/amiajnl-2014-002965
[Indexed for MEDLINE]
Free PMC Article

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