Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 578-589 |
| Number of pages | 12 |
| Journal | Pacific Symposium on Biocomputing |
| Volume | 0 |
| Issue number | 212669 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | 23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States Duration: Jan 3 2018 → Jan 7 2018 |
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Keywords
- Data integration
- Electronic medical records
- Knowledge graphs
- Visualization
ASJC Scopus subject areas
- Medicine(all)
Cite this
VisAGE : Integrating external knowledge into electronic medical record visualization. / Huang, Edward W.; Wang, Sheng; Zhai, Chengxiang.
In: Pacific Symposium on Biocomputing, Vol. 0, No. 212669, 01.01.2018, p. 578-589.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - VisAGE
T2 - Integrating external knowledge into electronic medical record visualization
AU - Huang, Edward W.
AU - Wang, Sheng
AU - Zhai, Chengxiang
PY - 2018/1/1
Y1 - 2018/1/1
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
UR - http://www.scopus.com/inward/record.url?scp=85048486528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048486528&partnerID=8YFLogxK
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
ER -