Developing a length of stay prediction model for newborns, achieving better accuracy with greater usability

Tzviel Frostig, Yoav Benjamini, Orli Kehat, Ahuva Weiss-Meilik, Dror Mandel, Ben Peleg, Zipora Strauss, Alexis Mitelpunkt

Research output: Contribution to journalArticlepeer-review


Background: One in ten newborn children is born prematurely. The elongated length of stay (LOS) of these children in the Neonatal Intensive Care Unit (NICU) has important implications on hospital occupancy figures, healthcare and management costs, as well as the psychology of parents. In order to allow accurate planning and resource allocation, this study aims to create a generalizable and robust model to predict the NICU LOS of preterm newborns. Methods: Data were collected from a large tertiary center NICU between 2011 and 2018 and relates to 5,362 newborns. The selected model was externally validated using a data set of 8,768 newborns from another tertiary center NICU. This report compares several models, such as Random Forest (RF), quantile RF, and other feature selection methods, including LASSO and AIC step-forward selection. In addition, a novel step-forward selection based on False Discovery Rate (FDR) for quantile regression is presented and evaluated. Results: A high-order quantile regression model for predicting preterm newborns’ LOS that uses only four features available at birth had more attractive properties than other richer ones. The model achieved a Mean Absolute Error (MAE) of 6.26 days on the internal validation set (average LOS 27.04) and an MAE of 6.04 days on the external validation set (average LOS 29.32). The suggested model surpassed the accuracy obtained by models in the literature. It is shown empirically that the FDR-based selection has better properties than the AIC-based step-forward selection approach. Conclusion: This paper demonstrates a process to create a predictive model for NICU LOS in preterm newborns, where each step is reasoned. We obtain a simple and robust model for NICU LOS prediction, which achieves far better results than the current model used for financing NICUs. Utilizing this model, we have created an easy-to-use online web application to ease parents' worries and to assist NICU management:

Original languageEnglish
Article number105267
JournalInternational Journal of Medical Informatics
StatePublished - Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023


  • Hospital Stay
  • Machine Learning
  • Neonatal Intensive Care Unit
  • Premature Birth
  • Statistical Model

ASJC Scopus subject areas

  • Health Informatics


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