title: Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting.
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.
  
  
  METHODS
  Non-endoscopic variables from patients with ALGIB attending the emergency departments of two teaching hospitals were analysed retrospectively for training/internal validation (n=170) and external validation (n=130) of the GB model. The performance of the GB algorithm in predicting recurrent bleeding, clinical intervention and severe bleeding was compared to a multiple logic regression (MLR) model and two published MLR-based prediction algorithms (BLEED and Strate prediction rule).
  
  
  RESULTS
  The GB algorithm had the best negative predictive values for the chosen outcomes (>88%). On internal validation the accuracy of the GB algorithm for predicting recurrent bleeding, therapeutic intervention and severe bleeding were (88%, 88% and 78% respectively) and superior to the BLEED classification (64%, 68% and 63%), Strate prediction rule (78%, 78%, 67%) and conventional MLR (74%, 74% 62%). On external validation the accuracy was similar to conventional MLR for recurrent bleeding (88% vs. 83%) and therapeutic intervention (91% vs. 87%) but superior for severe bleeding (83% vs. 71%).
  
  
  CONCLUSIONS
  The gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models. These may be useful for risk stratification of patients on presentation to the emergency department.
authors.affiliation: Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
authors.forename: Lakshmana
authors.initials: L
authors.lastname: Ayaru
authors.affiliation: Department of Biomedical Engineering, Kings College London, London, United Kingdom.
authors.forename: Petros-Pavlos
authors.initials: PP
authors.lastname: Ypsilantis
authors.affiliation: Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
authors.forename: Abigail
authors.initials: A
authors.lastname: Nanapragasam
authors.affiliation: Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
authors.forename: Ryan Chang-Ho
authors.initials: RC
authors.lastname: Choi
authors.affiliation: Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
authors.forename: Anish
authors.initials: A
authors.lastname: Thillanathan
authors.affiliation: Department of Gastroenterology, Charing Cross and Hammersmith Hospitals, Imperial College Healthcare NHS Trust, London, United Kingdom.
authors.forename: Lee
authors.initials: L
authors.lastname: Min-Ho
authors.affiliation: Department of Biomedical Engineering, Kings College London, London, United Kingdom.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Montana
mesh_terms: D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D005260:Female; D006471:Gastrointestinal Hemorrhage / Q000175:diagnosis*; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D011379:Prognosis; D055815:Young Adult