The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.
نویسندگان
چکیده
Cost-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.
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ورودعنوان ژورنال:
- Proceedings. Symposium on Computer Applications in Medical Care
دوره شماره
صفحات -
تاریخ انتشار 1995