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Decision support through risk cost estimation in 30-day hospital unplanned readmission

Author

Listed:
  • Laura Arnal
  • Pedro Pons-Suñer
  • J Ramón Navarro-Cerdán
  • Pablo Ruiz-Valls
  • Mª Jose Caballero Mateos
  • Bernardo Valdivieso Martínez
  • Juan-Carlos Perez-Cortes

Abstract

Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient’s readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.

Suggested Citation

  • Laura Arnal & Pedro Pons-Suñer & J Ramón Navarro-Cerdán & Pablo Ruiz-Valls & Mª Jose Caballero Mateos & Bernardo Valdivieso Martínez & Juan-Carlos Perez-Cortes, 2022. "Decision support through risk cost estimation in 30-day hospital unplanned readmission," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0271331
    DOI: 10.1371/journal.pone.0271331
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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