A Case Study of Stochastic Optimization in Health Policy: Problem Formulation and Preliminary Results

Draper, D; Fouskakis, D

dc.contributor.author	Draper, D	en
dc.contributor.author	Fouskakis, D	en
dc.date.accessioned	2014-03-01T01:49:06Z
dc.date.available	2014-03-01T01:49:06Z
dc.date.issued	2000	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25674
dc.subject	bayesian decision theory	en
dc.subject	Case Study	en
dc.subject	Cross Validation	en
dc.subject	Data Collection	en
dc.subject	Expected Utility	en
dc.subject	Expected Value	en
dc.subject	Genetic Algorithm	en
dc.subject	Health Policy	en
dc.subject	Hospital Admission	en
dc.subject	Hospital Care	en
dc.subject	Input Output Analysis	en
dc.subject	Logistic Regression	en
dc.subject	Monte Carlo Method	en
dc.subject	Mortality Rate	en
dc.subject	Prediction Accuracy	en
dc.subject	Quality of Health Care	en
dc.subject	Sensitivity Analysis	en
dc.subject	Simulated Annealing	en
dc.subject	Stochastic Optimization	en
dc.subject	Threshold Accepting	en
dc.subject	Variable Selection	en
dc.subject	tabu search	en
dc.title	A Case Study of Stochastic Optimization in Health Policy: Problem Formulation and Preliminary Results	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/A:1026504402220	en
heal.identifier.secondary	http://dx.doi.org/10.1023/A:1026504402220	en
heal.publicationDate	2000	en
heal.abstract	We use Bayesian decision theory to address a variable selection problem arising in attempts to indirectly measure the quality of hospital care, by comparing observed mortality rates to expected values based on patient sickness at admission. Our method weighs data collection costs against predictive accuracy to find an optimal subset of the available admission sickness variables. The approach involves maximizing	en
heal.journalName	Journal of Global Optimization	en
dc.identifier.doi	10.1023/A:1026504402220	en