Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 956-960.
Abstract
The paper presents a support vector method for estimating probabilities in a real world problem: the prediction of probability of survival in critically ill patients. The standard procedure with Support Vectors Machines uses Platt's method to fit a sigmoid that transforms continuous outputs into probabilities. The method proposed here exploits the difference between maximizing the AUC and minimizing the error rate in binary classification tasks. The conclusion is that it is preferable to optimize the AUC first (using a multivariate SVM) to then fit a sigmoid. We provide experimental evidence in favor of our proposal. For this purpose, we used data collected in general ICUs at 10 hospitals in Spain; 6 of these include coronary patients, while the other 4 do not treat coronary diseases. The total number of patients considered in our study was 2501.
@inproceedings{ijcai2007p154,
title = {Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve},
author = {Luaces, Oscar and Quevedo, José Ramón and Taboada, Francisco and Albaiceta, Guillermo M. and Bahamonde, Antonio},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {956--960},
year = {2007},
url = {https://ijcai.org/proceedings/2007/154},
}
TY - CPAPER TI - Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve AU - Luaces, Oscar AU - Quevedo, José Ramón AU - Taboada, Francisco AU - Albaiceta, Guillermo M. AU - Bahamonde, Antonio T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 956 EP - 960 UR - https://ijcai.org/proceedings/2007/154 ER -Copied
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