Name
Title | ||
|---|---|---|
Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks. |
Abstract | ||
|---|---|---|
Given the strong adverse effect of pressure ulcers on patients and the high cost for treating pressure ulcers, our Bayesian network based model provides a novel framework for significantly improving the sensitivity of the prediction model. Thus, when the model is deployed in a clinical setting, the caregivers can suitably respond to conditions likely associated with pressure ulcer incidence. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12911-017-0471-z | BMC Med. Inf. & Decision Making |
Keywords | Field | DocType |
Bayesian networks,Electronic health records,Intensive care units,Model learning,Pressure ulcers | Data mining,Intensive care unit,Bayesian network,Medical record,Health informatics,Medicine,Model learning | Journal |
Volume | Issue | Citations |
17 | 2 | 1 |
PageRank | References | Authors |
0.37 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pacharmon Kaewprag | 1 | 1 | 0.37 |
| Cheryl Newton | 2 | 1 | 0.37 |
| Brenda Vermillion | 3 | 1 | 0.70 |
| Sookyung Hyun | 4 | 5 | 1.80 |
| Kun Huang | 5 | 100 | 8.78 |
| Raghu Machiraju | 6 | 864 | 78.64 |