TY - JOUR
T1 - VisAGE
T2 - 23rd Pacific Symposium on Biocomputing, PSB 2018
AU - Huang, Edward W.
AU - Wang, Sheng
AU - Zhai, Cheng Xiang
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1144245. This research was also supported by grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov).
Publisher Copyright:
© 2017 The Authors.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data integration
KW - Electronic medical records
KW - Knowledge graphs
KW - Visualization
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U2 - 10.1142/9789813235533_0053
DO - 10.1142/9789813235533_0053
M3 - Conference article
C2 - 29218916
AN - SCOPUS:85048486528
VL - 0
SP - 578
EP - 589
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
SN - 2335-6936
IS - 212669
Y2 - 3 January 2018 through 7 January 2018
ER -