IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04325691.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04325691/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sheelu Sagar & Rohit Rastogi & Vikas Garg & Ishwar V. Basavaraddi, 2022. "Impact of Meditation on Quality of Life of Employees," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(1), pages 1-16, January.
    2. Vinícius Serafini Roglio & Eduardo Nunes Borges & Francisco Diego Rabelo-da-Ponte & Felipe Ornell & Juliana Nichterwitz Scherer & Jaqueline Bohrer Schuch & Ives Cavalcante Passos & Breno Sanvicente-Vi, 2020. "Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.
    3. Alexander Engels & Katrin C Reber & Ivonne Lindlbauer & Kilian Rapp & Gisela Büchele & Jochen Klenk & Andreas Meid & Clemens Becker & Hans-Helmut König, 2020. "Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.

    More about this item

    Keywords

    Machine learning; neural network; prediction; health services research; public health;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04325691. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.