Name
Title | ||
|---|---|---|
Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements. |
Abstract | ||
|---|---|---|
Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12911-019-0733-z | BMC Med. Inf. & Decision Making |
Keywords | Field | DocType |
Acute kidney injury,Artificial neural networks,Intensive care unit,Multivariate logistic regression,Physiological measurements,Predictive modeling,Random forest | Intensive care unit,Acute kidney injury,Emergency medicine,Multivariate statistics,Knowledge management,Health informatics,Medicine,Logistic regression | Journal |
Volume | Issue | ISSN |
19S | 1 | 1472-6947 |
Citations | PageRank | References |
1 | 0.39 | 6 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lindsay P Zimmerman | 1 | 2 | 0.87 |
| Paul Reyfman | 2 | 1 | 0.39 |
| Angela Smith | 3 | 8 | 2.54 |
| Zexian Zeng | 4 | 18 | 5.03 |
| Abel N Kho | 5 | 316 | 41.41 |
| L. Nelson Sanchez-Pinto | 6 | 1 | 0.39 |
| Yuan Luo | 7 | 136 | 22.83 |