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Machine-learning prediction for hospital length of stay using a French medico-administrative database

Author

Listed:
  • Franck Jaotombo

    (EM - EMLyon Business School)

  • Vanessa Pauly
  • Guillaume Fond
  • Veronica Orleans
  • Pascal Auquier
  • Badih Ghattas
  • Laurent Boyer

Abstract

"Introduction: Prolonged Hospital Length of Stay (PLOS) is an indicator of deteriorated efficiency in Quality of Care. One goal of public health management is to reduce PLOS by identifying its most relevant predictors. The objective of this study is to explore Machine Learning (ML) models that best predict PLOS.Methods: Our dataset was collected from the French Medico-Administrative database (PMSI) as a retrospective cohort study of all discharges in the year 2015 from a large university hospital in France (APHM). The study outcomes were LOS transformed into a binary variable (long vs. short LOS) according to the 90th percentile (14 days). Logistic regression (LR), classification and regression trees (CART), random forest (RF), gradient boosting (GB) and neural networks (NN) were applied to the collected data. The predictive performance of the models was evaluated using the area under the ROC curve (AUC).Results: Our analysis included 73,182 hospitalizations, of which 7,341 (10.0%) led to PLOS. The GB classifier was the most performant model with the highest AUC (0.810), superior to all the other models (all p-values

Suggested Citation

  • Franck Jaotombo & Vanessa Pauly & Guillaume Fond & Veronica Orleans & Pascal Auquier & Badih Ghattas & Laurent Boyer, 2023. "Machine-learning prediction for hospital length of stay using a French medico-administrative database," Post-Print hal-04325691, HAL.
    • Handle: RePEc:hal:journl:hal-04325691
    Note: View the original document on HAL open archive server: https://hal.science/hal-04325691
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    References listed on IDEAS

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    1. Baumann, Aron & Wyss, Kaspar, 2021. "The shift from inpatient care to outpatient care in Switzerland since 2017: Policy processes and the role of evidence," Health Policy, Elsevier, vol. 125(4), pages 512-519.
    2. Laura Acion & Diana Kelmansky & Mark van der Laan & Ethan Sahker & DeShauna Jones & Stephan Arndt, 2017. "Use of a machine learning framework to predict substance use disorder treatment success," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
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    Keywords

    Machine learning; neural network; prediction; health services research; public health;
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