VisAGE: Integrating external knowledge into electronic medical record visualization

Edward W. Huang, Sheng Wang, Cheng Xiang Zhai

Research output: Contribution to journalConference articlepeer-review


In this paper, we present VisAGE, a method that visualizes electronic medical records (EMRs) in a low-dimensional space. Effective visualization of new patients allows doctors to view similar, previously treated patients and to identify the new patients’ disease subtypes, reducing the chance of misdiagnosis. However, EMRs are typically incomplete or fragmented, resulting in patients who are missing many available features being placed near unrelated patients in the visualized space. VisAGE integrates several external data sources to enrich EMR databases to solve this issue. We evaluated VisAGE on a dataset of Parkinson’s disease patients. We qualitatively and quantitatively show that VisAGE can more effectively cluster patients, which allows doctors to better discover patient subtypes and thus improve patient care.

Original languageEnglish (US)
Pages (from-to)578-589
Number of pages12
JournalPacific Symposium on Biocomputing
Issue number212669
StatePublished - 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018


  • Data integration
  • Electronic medical records
  • Knowledge graphs
  • Visualization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics


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