Name
Title | ||
|---|---|---|
Improving Clinical Relevance in Ensemble Support Vector Machine Models of Radiation Pneumonitis Risk |
Abstract | ||
|---|---|---|
Patients undergoing thoracic radiation therapy can develop radiation pneumonitis (RP), a potentially fatal inflammation of the lungs. Support vector machines (SVMs), a statistical machine learning method, have recently been used to build binary-outcome RP prediction models with promising results. In this work, we (1) introduce a feature-ranking selection step to limit complexity in ensemble SVM models (2) show that ensembles of SVMs provide a statistically significant performance improvement in the area under the cross-validated receiver operating curve and (3) apply Platt's tuning to generate probability estimates from the component SVMs in order to augment clinical relevance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICMLA.2009.74 | ICMLA |
Keywords | Field | DocType |
biological effects of radiation,injuries,lung,medical computing,probability,radiation therapy,risk analysis,support vector machines,Platt tuning,binary outcome prediction models,cross-validated receiver operating curve,ensemble SVM model,feature ranking selection,lung inflammation,probability estimates,radiation pneumonitis risk model,statistical machine learning method,support vector machine,thoracic radiation therapy,biological effects of radiation,modeling,probability | Data mining,Receiver operating characteristic,Computer science,Clinical significance,Artificial intelligence,Probabilistic logic,Predictive modelling,Kernel (linear algebra),Radiation Pneumonitis,Pattern recognition,Support vector machine,Machine learning,Performance improvement | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Todd W. Schiller | 1 | 47 | 3.16 |
| Yixen Chen | 2 | 0 | 0.34 |
| Issam El-Naqa | 3 | 528 | 36.31 |
| Joseph O. Deasy | 4 | 105 | 14.98 |