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Using machine learning to predict nosocomial infections and medical accidents in a NICU

Author

Listed:
  • Marc Beltempo
  • Georges Bresson

    (TEPP - Travail, Emploi et Politiques Publiques - UPEM - Université Paris-Est Marne-la-Vallée - CNRS - Centre National de la Recherche Scientifique, CRED - Centre de Recherche en Economie et Droit - Université Paris-Panthéon-Assas)

  • Guy Lacroix

    (ULaval - Université Laval [Québec])

Abstract

Background: Adult studies have shown that nursing overtime and unit overcrowding is associated with increased adverse patient events but there exists little evidence for the Neonatal Intensive Care Unit (NICU). Objectives: To predict the onset on nosocomial infections and medical accidents in a NICU using machine learning models. Subjects: Retrospective study on the 7,438 neonates admitted in the CHU de Québec NICU (capacity of 51 beds) from 10 April 2008 to 28 March 2013. Daily administrative data on nursing overtime hours, total regular hours, number of admissions, patient characteristics, as well as information on nosocomial infections and on the timing and type of medical errors were retrieved from various hospital-level datasets. Methodology: We use a generalized mixed effects regression tree model (GMERT) to elaborate predictions trees for the two outcomes. Neonates' characteristics and daily exposure to numerous covariates are used in the model. GMERT is suitable for binary outcomes and is a recent extension of the standard tree-based method. The model allows to determine the most important predictors. Results: DRG severity level, regular hours of work, overtime, admission rates, birth weight and occupation rates are the main predictors for both outcomes. On the other hand, gestational age, C-Section, multiple births, medical/surgical and number of admissions are poor predictors. Conclusion: Prediction trees (predictors and split points) provide a useful management tool to prevent undesirable health outcomes in a NICU.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Marc Beltempo & Georges Bresson & Guy Lacroix, 2023. "Using machine learning to predict nosocomial infections and medical accidents in a NICU," Post-Print hal-04103625, HAL.
  • Handle: RePEc:hal:journl:hal-04103625
    DOI: 10.1007/s12553-022-00723-1
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    References listed on IDEAS

    as
    1. Evans, William N. & Kim, Beomsoo, 2006. "Patient outcomes when hospitals experience a surge in admissions," Journal of Health Economics, Elsevier, vol. 25(2), pages 365-388, March.
    2. Haizhen Lin, 2014. "Revisiting the relationship between nurse staffing and quality of care in nursing homes: An instrumental variables approach," Working Papers 2014-01, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    3. Lin, Haizhen, 2014. "Revisiting the relationship between nurse staffing and quality of care in nursing homes: An instrumental variables approach," Journal of Health Economics, Elsevier, vol. 37(C), pages 13-24.
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    Cited by:

    1. Marc Beltempo & Georges Bresson & Jean-Michel Étienne & Guy Lacroix, 2022. "Infections, accidents and nursing overtime in a neonatal intensive care unit," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(4), pages 627-643, June.

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    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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