Ambrosino R, Buchanan B G, Cooper G F, Fine M J
Section of Medical Informatics, University of Pittsburgh, USA.
Proc Annu Symp Comput Appl Med Care. 1995:304-8.
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.
具有成本效益的医疗保健是当今重要的健康相关问题的前沿。匹兹堡大学的一个研究团队一直致力于通过试图确定适合门诊治疗且安全的社区获得性肺炎患者子集来降低医疗成本。该领域对敏感性和特异性的要求使得难以使用基于规则的学习算法以及基于准确性的标准性能度量。本文描述了如何使用误分类成本来协助基于规则的机器学习程序推导决策支持辅助工具，以帮助为社区获得性肺炎患者选择门诊治疗。
