Name
Abstract | ||
|---|---|---|
•We suggest an approach to identify intradialytic systolic BP patterns.•We classify ESRD patients based on dense sequences in EHR data.•We demonstrate that models with BP patterns have a better prediction of cerebrovascular events.•We suggest that our clustering approach is generalizable to analyses of dense EHR data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.jbi.2018.05.013 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Hemodialysis,Intradialytic blood pressure patterns,Dynamic time warping,Density peak clustering algorithm | Time series,Covariate,Dynamic time warping,Pattern recognition,Computer science,Kidney disease,Blood pressure,Artificial intelligence,Cluster analysis | Journal |
Volume | ISSN | Citations |
83 | 1532-0464 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Wang | 1 | 53 | 5.87 |
| Jing-yi Zhou | 2 | 1 | 0.36 |
| Yu Tian | 3 | 3 | 3.11 |
| Yu Wang | 4 | 167 | 15.47 |
| zhang ping | 5 | 73 | 27.08 |
| Jianghua Chen | 6 | 1 | 2.05 |
| Jing-Song Li | 7 | 222 | 11.02 |