The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.
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.
Ambrosino
R
R
Section of Medical Informatics, University of Pittsburgh, USA.
Buchanan
B G
BG
Cooper
G F
GF
Fine
M J
MJ
D000465:Algorithms; D001185:Artificial Intelligence; D017714:Community-Acquired Infections / Q000145:classification* / Q000175:diagnosis / Q000628:therapy; D003362:Cost-Benefit Analysis; D003661:Decision Support Techniques; D003951:Diagnostic Errors; D006760:Hospitalization / Q000191:economics; D006801:Humans; D011014:Pneumonia / Q000145:classification* / Q000175:diagnosis / Q000628:therapy; D011237:Predictive Value of Tests; D012372:ROC Curve; D012680:Sensitivity and Specificity