Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data.

Journal: PloS one
PMID: 38032876

Abstract

OBJECTIVE: This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data.

Authors

  • Franck Jaotombo
    Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, 27, boulevard Jean-Moulin.
  • Luca Adorni
    Becker Friedman Institute, Chicago, IL, United States of America.
  • Badih Ghattas
    Mathematics Institute of Marseille, Aix-Marseille University, Marseille, France.
  • Laurent Boyer
    Aix-Marseille University, EA 3279 - Public Health, Chronic Diseases and Quality of Life - Research Unit, La Timone Medical University, 27, boulevard Jean-Moulin.

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