Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature subset selection for learning preferences: a case study
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Journal of Biomedical Informatics
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Adapting decision DAGs for multipartite ranking
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Assessment of cardiovascular disease risk prediction models: evaluation methods
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Computers in Biology and Medicine
Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease
Expert Systems with Applications: An International Journal
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Objective: Survival probability predictions in critically ill patients are mainly used to measure the efficacy of intensive care unit (ICU) treatment. The available models are functions induced from data on thousands of patients. Eventually, some of the variables used for these purposes are not part of the clinical routine, and may not be registered in some patients. In this paper, we propose a new method to build scoring functions able to make reliable predictions, though functions whose induction only requires records from a small set of patients described by a few variables. Methods: We present a learning method based on the use of support vector machines (SVM), and a detailed study of its prediction performance, in different contexts, of groups of variables defined according to the source of information: monitoring devices, laboratory findings, and demographic and diagnostic features. Results: We employed a data set collected in general ICUs at 10 units of hospitals in Spain, 6 of which include coronary patients, while the other 4 do not treat coronary diseases. The total number of patients considered in our study was 2501, 19.83% of whom did not survive. Using these data, we report a comparison between the SVM method proposed here with other approaches based on logistic regression (LR), including a second-level recalibration of release III of the acute physiology and chronic health evaluation (APACHE, a scoring system commonly used in ICUs) induced from the available data. The SVM method significantly outperforms them all from a statistical point of view. Comparison with the commercial version of APACHE III shows that the SVM scores are slightly better when working with data sets of more than 500 patients. Conclusions: From a practical point of view, the implications of the research reported here may be helpful to address the construction of cheap and reliable prediction systems in accordance with the peculiarities of ICUs and kinds of patients.