Name
Title | ||
|---|---|---|
Optimising Classifiers for the Detection of Physiological Deterioration in Patient Vital-sign Data. |
Abstract | ||
|---|---|---|
Hospital patient outcomes can be improved by the early identification of physiological deterioration. Automatic methods of detecting patient deterioration in vital-sign data typically attempt to identify deviations from assumed "normal" physiological condition. This paper investigates the use of a multi-class approach, in which "abnormal" physiology is modelled explicitly. The success of such a method relies on the accuracy of data annotations provided by clinical experts. We propose an approach to estimate class labels provided by clinicians, and refine those labels such they may be used to optimise a multi-class classifier for identifying patient deterioration. We demonstrate the effectiveness of the proposed methods using a large data-set acquired in a 24-bed step-down unit. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.5220/0003138904250428 | BIOSIGNALS 2011 |
Keywords | DocType | Citations |
Novelty detection,Multi-class classification,SVM,MLP | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S Khalid | 1 | 2 | 2.14 |
| D Clifton | 2 | 222 | 24.26 |
| L Clifton | 3 | 202 | 12.53 |
| Lionel Tarassenko | 4 | 643 | 118.09 |