Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting.

Journal: PloS one
Published Date: Jul 14, 2015

Abstract

BACKGROUND: There are no widely used models in clinical care to predict outcome in acute lower gastro-intestinal bleeding (ALGIB). If available these could help triage patients at presentation to appropriate levels of care/intervention and improve medical resource utilisation. We aimed to apply a state-of-the-art machine learning classifier, gradient boosting (GB), to predict outcome in ALGIB using non-endoscopic measurements as predictors.

Authors

  • Lakshmana Ayaru
    Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
  • Petros-Pavlos Ypsilantis
    Department of Biomedical Engineering, King's College London, London SE1 7EH, United Kingdom.
  • Abigail Nanapragasam
    Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
  • Ryan Chang-Ho Choi
    Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
  • Anish Thillanathan
    Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
  • Lee Min-Ho
    Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
  • Giovanni Montana
    Department of Biomedical Engineering, King's College London, London SE1 7EH, United Kingdom.

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