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조인숙 ( Cho In-Sook ) - 인하대학교 의과대학 간호학과
정은자 ( Jung Eun-Ja ) - 분당서울대학교병원
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Abstract
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Purpose: The study was designed to determine the discriminating ability of a Bayesian network (BN) for predicting risk for pressure ulcers.
Methods: Analysis was done using a retrospective cohort, nursing records representing 21,114 hospital days, 3,348 patients at risk for ulcers, admitted to the intensive care unit of a tertiary teaching hospital between January 2004 and January 2007. A BN model and two logistic regression (LR) versions, model-I and -II, were compared, varying the nature, number and quality of input variables. Classification competence and case coverage of the models were tested and compared using a threefold cross validation method.
Results: Average incidence of ulcers was 6.12%. Of the two LR models, model-I demonstrated better indexes of statistical model fits. The BN model had a sensitivity of 81.95%, specificity of 75.63%, positive and negative predictive values of 35.62% and 96.22% respectively. The area under the receiver operating characteristic (AUROC) was 85.01% implying moderate to good overall performance, which was similar to LR model-I. However, regarding case coverage, the BN model was 100% compared to 15.88% of LR.
Conslusion: Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.
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KeyWords
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Pressure ulcer, Bayesian prediction, Logistic models, Risk assessment, Data mining
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원문 및 링크아웃 정보
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등재저널 정보
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