Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

Journal: BMJ health & care informatics
PMID: 39632097

Abstract

OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich feature patient data sets more readily available. These large data stores lend themselves to use in modern machine learning (ML) models. This paper investigates the use of transformer-based models to identify critical deterioration in unplanned ED admissions, using free-text fields, such as triage notes, and tabular data, including early warning scores (EWS).

Authors

  • Matthew Watson
    Department of Computer Science, Durham University, Durham, UK.
  • Stelios Boulitsakis Logothetis
    Department of Public Health and Primary Care, Cambridge University, Cambridge, UK.
  • Darren Green
    Donal O'Donoghue Renal Research Centre & Department of Renal Medicine, Northern Care Alliance NHS Foundation Trust, Salford M6 8HD, United Kingdom.
  • Mark Holland
    School of Clinical and Biomedical Sciences, University of Bolton, Bolton, UK.
  • Pinkie Chambers
    Cancer Division, University College London Hospitals NHS Foundation Trust, London, UK.
  • Noura Al Moubayed
    Department of Computer Science, Durham University, Durham, UK.

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