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Predictive Bayesian Network Model Using Electronic Patient Records for Prevention of Hospital-Acquired Pressure Ulcers
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In Sook Cho, Eunja Chung
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J Korean Acad Nurs 2011;41(3):423-431. Published online June 13, 2011
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DOI: https://doi.org/10.4040/jkan.2011.41.3.423
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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.
Conclusion
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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Citations
Citations to this article as recorded by  - Development of a Pressure Injury Machine Learning Prediction Model and Integration into Clinical Practice: A Prediction Model Development and Validation Study
Ju Hee Lee, Jae Yong Yu, So Yun Shim, Kyung Mi Yeom, Hyun A Ha, Se Yong Jekal, Ki Tae Moon, Joo Hee Park, Sook Hyun Park, Jeong Hee Hong, Mi Ra Song, Won Chul Cha
Korean Journal of Adult Nursing.2024; 36(3): 191. CrossRef - Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta‐analysis
Yuxia Ma, Xiang He, Tingting Yang, Yifang Yang, Ziyan Yang, Tian Gao, Fanghong Yan, Boling Yan, Juan Wang, Lin Han
Journal of Clinical Nursing.2024;[Epub] CrossRef - The predictive effect of different machine learning algorithms for pressure injuries in hospitalized patients: A network meta-analyses
Chaoran Qu, Weixiang Luo, Zhixiong Zeng, Xiaoxu Lin, Xuemei Gong, Xiujuan Wang, Yu Zhang, Yun Li
Heliyon.2022; 8(11): e11361. CrossRef - Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit
Mireia Ladios-Martin, José Fernández-de-Maya, Francisco-Javier Ballesta-López, Adrián Belso-Garzas, Manuel Mas-Asencio, María José Cabañero-Martínez
American Journal of Critical Care.2020; 29(4): e70. CrossRef - Development and Evaluation of Electronic Health Record Data-Driven Predictive Models for Pressure Ulcers
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
Journal of Korean Academy of Nursing.2019; 49(5): 575. CrossRef - Development and Comparison of Predictive Models for Pressure Injuries in Surgical Patients
Seul Ki Park, Hyeoun-Ae Park, Hee Hwang
Journal of Wound, Ostomy & Continence Nursing.2019; 46(4): 291. CrossRef - Automated Pressure Injury Risk Assessment System Incorporated Into an Electronic Health Record System
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Nursing Research.2017; 66(6): 462. CrossRef - Recommendation of Personalized Surveillance Interval of Colonoscopy via Survival Analysis
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Journal of Korean Institute of Industrial Engineers.2016; 42(2): 129. CrossRef - Medical Data Based Clinical Pathway Analysis and Automatic Ganeration System
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The Journal of Korea Information and Communications Society.2014; 39C(6): 497. CrossRef - Reusability of EMR Data for Applying Cubbin and Jackson Pressure Ulcer Risk Assessment Scale in Critical Care Patients
Eunkyung Kim, Mona Choi, JuHee Lee, Young Ah Kim
Healthcare Informatics Research.2013; 19(4): 261. CrossRef - Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model
Insook Cho, Ihnsook Park, Eunman Kim, Eunjoon Lee, David W. Bates
International Journal of Medical Informatics.2013; 82(11): 1059. CrossRef
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