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Applicability of machine learning technique in the screening of patients with mild traumatic brain injury

Author

Listed:
  • Miriam Leiko Terabe
  • Miyoko Massago
  • Pedro Henrique Iora
  • Thiago Augusto Hernandes Rocha
  • João Vitor Perez de Souza
  • Lily Huo
  • Mamoru Massago
  • Dalton Makoto Senda
  • Elisabete Mitiko Kobayashi
  • João Ricardo Vissoci
  • Catherine Ann Staton
  • Luciano de Andrade

Abstract

Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.

Suggested Citation

  • Miriam Leiko Terabe & Miyoko Massago & Pedro Henrique Iora & Thiago Augusto Hernandes Rocha & João Vitor Perez de Souza & Lily Huo & Mamoru Massago & Dalton Makoto Senda & Elisabete Mitiko Kobayashi &, 2023. "Applicability of machine learning technique in the screening of patients with mild traumatic brain injury," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0290721
    DOI: 10.1371/journal.pone.0290721
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    References listed on IDEAS

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    1. Latifa A AlKaabi & Lina S Ahmed & Maryam F Al Attiyah & Manar E Abdel-Rahman, 2020. "Predicting hypertension using machine learning: Findings from Qatar Biobank Study," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    2. Marcela Bergamini & Pedro Henrique Iora & Thiago Augusto Hernandes Rocha & Yolande Pokam Tchuisseu & Amanda de Carvalho Dutra & João Felipe Herman Costa Scheidt & Oscar Kenji Nihei & Maria Dalva de Ba, 2020. "Mapping risk of ischemic heart disease using machine learning in a Brazilian state," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-15, December.
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