Name
Abstract | ||
|---|---|---|
Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/978-1-61499-432-9-53 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Bayesian Networks,Clinical Evidence,Knowledge Engineering,Prognostic Models,Clinical Decision Support | Data mining,Decision support system,Clinical Practice,Bayesian network,Medicine,Evidence-based medicine,Bayes' theorem | Conference |
Volume | ISSN | Citations |
205 | 0926-9630 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Barbaros Yet | 1 | 46 | 6.53 |
| Zane Perkins | 2 | 11 | 2.31 |
| Nigel Tai | 3 | 11 | 2.65 |
| William Marsh | 4 | 250 | 16.57 |