Name
Title | ||
|---|---|---|
Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models. |
Abstract | ||
|---|---|---|
•We propose a novel unsupervised approach to EMR data: composite mixture models (CMM).•CMMs enable unbiased discovery of latent, uncataloged patient phenotypes in sepsis.•Cluster analysis reveals physiological and temporal trends of sepsis mortality risk.•CMMs demonstrate competitive missing data imputation performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jbi.2017.11.015 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Electronic health records,Mixture modeling,Risk stratification,Sepsis,Composite mixture model,Cluster analysis | Health care,Risk of mortality,Data mining,Computer science,Medical record,Multivariate analysis,Sepsis,Mixture model,Probabilistic framework | Journal |
Volume | ISSN | Citations |
78 | 1532-0464 | 1 |
PageRank | References | Authors |
0.35 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael B. Mayhew | 1 | 1 | 0.69 |
| Brenden K. Petersen | 2 | 1 | 0.35 |
| Ana Paula Sales | 3 | 1 | 0.69 |
| John D. Greene | 4 | 1 | 0.35 |
| Vincent Liu | 5 | 10 | 3.40 |
| Todd Wasson | 6 | 2 | 0.70 |