Using Machine Learning to Predict Nosocomial Infections and Medical Accidents in a Nicu

18 Pages Posted: 6 Apr 2020

See all articles by Marc Beltempo

Marc Beltempo

McGill University

Georges Bresson

ERMES (CNRS), Université Panthéon-Assas Paris II

Guy Lacroix

Université Laval - Département d'Économique; IZA Institute of Labor Economics

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.

Keywords: neonatal health outcomes, nursing overtime, machine learning, mixed effects regression tree

JEL Classification: I1, J2, C11, C14, C23

Suggested Citation

Beltempo, Marc and Bresson, Georges and Lacroix, Guy, Using Machine Learning to Predict Nosocomial Infections and Medical Accidents in a Nicu. IZA Discussion Paper No. 13099. Available at SSRN: https://ssrn.com/abstract=3568304

Marc Beltempo (Contact Author)

McGill University

1001 Sherbrooke St. W
Montreal
Canada

Georges Bresson

ERMES (CNRS), Université Panthéon-Assas Paris II ( email )

12 Place du Panthéon
Paris, Cedex 5, 75005
France

Guy Lacroix

Université Laval - Département d'Économique ( email )

2325 Rue de l'Université
Ste-Foy, Quebec G1K 7P4 G1K 7P4
Canada
418-656-2024 (Phone)
418-656-7798 (Fax)

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

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