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pubmed-article:8563290pubmed:abstractTextCost-effective health care is at the forefront of today's important health-related issues. A research team at the University of Pittsburgh has been interested in lowering the cost of medical care by attempting to define a subset of patients with community-acquire pneumonia for whom outpatient therapy is appropriate and safe. Sensitivity and specificity requirements for this domain make it difficult to use rule-based learning algorithms with standard measures of performance based on accuracy. This paper describes the use of misclassification costs to assist a rule-based machine-learning program in deriving a decision-support aid for choosing outpatient therapy for patients with community-acquired pneumonia.lld:pubmed
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pubmed-article:8563290pubmed:dateRevised2009-11-18lld:pubmed
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pubmed-article:8563290pubmed:articleTitleThe use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.lld:pubmed
pubmed-article:8563290pubmed:affiliationSection of Medical Informatics, University of Pittsburgh, USA.lld:pubmed
pubmed-article:8563290pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:8563290pubmed:publicationTypeResearch Support, U.S. Gov't, P.H.S.lld:pubmed
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