首页|Efficient and sparse feature selection for biomedical text classification via the elastic net: Application to ICU risk stratification from nursing notes
Efficient and sparse feature selection for biomedical text classification via the elastic net: Application to ICU risk stratification from nursing notes
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NSTL
Elsevier
Background and significance: Sparsity is often a desirable property of statistical models, and various feature selection methods exist so as to yield sparser and interpretable models. However, their application to biomedical text classification, particularly to mortality risk stratification among intensive care unit (ICU) patients, has not been thoroughly studied.
Text miningFeature selectionElastic netICURisk stratificationMachine learning
Dudley, R. Adams、Marafino, Ben J.、Boscardin, W. John
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Univ Calif San Francisco, Philip R Lee Inst Hlth Policy Studies, Sch Med, San Francisco, CA 94118
Univ Calif San Francisco, Dept Epidemiol & Biostat, San Francisco, CA 94118 USA
NSTL
Elsevier
