Journal of Korean Academy of Nursing (대한간호학회지)

Korean Society of Nursing Science (한국간호과학회)
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Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers

전자의무기록을 이용한 욕창발생 예측 베이지안 네트워크 모델 개발

  • Cho, In-Sook (Department of Nursing, Inha University) ;
  • Chung, Eun-Ja (Department of Nursing, Seoul National University Bundang Hospital)
  • 조인숙 (인하대학교 의과대학 간호학과) ;
  • 정은자 (분당서울대학교병원)
  • Received : 2010.07.16
  • Accepted : 2011.06.07
  • Published : 2011.06.30
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Abstract

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. Conclusion: Discriminating ability of the BN model was found to be acceptable and case coverage proved to be excellent for clinical use.

Keywords

References

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