Name
Abstract | ||
|---|---|---|
•We develop a continuous time unsupervised learning model for heterogenous and irregularly time sampled electronic health record data.•The model is a joint distribution between two variables and their inter-measurement time.•Data from the United States Veterans Health Administration is used to train the model.•Time-dependent likelihood ratio maps are produced for minimal vs moderate-severe depression. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jbi.2022.104084 | Journal of Biomedical Informatics |
Keywords | DocType | Volume |
Electronic health records,Probabilistic models,Mixture models,Time-dependent modeling | Journal | 130 |
ISSN | Citations | PageRank |
1532-0464 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alan D. Kaplan | 1 | 0 | 0.34 |
| Uttara Tipnis | 2 | 0 | 0.34 |
| Jean C. Beckham | 3 | 0 | 0.34 |
| Nathan A. Kimbrel | 4 | 0 | 0.34 |
| David W. Oslin | 5 | 0 | 0.34 |
| the MVP Suicide Exemplar Workgroup | 6 | 0 | 0.34 |
| Benjamin H. McMahon | 7 | 0 | 0.34 |