Prediction of Outcome in XXXX Using Gradient Boosting.

@entity1 @entity360 @entity548
BACKGROUND: There are no widely used models in clinical care to predict outcome in @entity548 ( @entity548 ). If available these could help triage @entity1 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 @entity548 using non-endoscopic measurements as predictors. METHODS: Non-endoscopic variables from @entity1 with @entity548 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 @entity548 , clinical intervention and severe @entity548 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 @entity548 , therapeutic intervention and severe @entity548 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 @entity548 (88% vs. 83%) and therapeutic intervention (91% vs. 87%) but superior for severe @entity548 (83% vs. 71%). CONCLUSION: The gradient boosting algorithm accurately predicts outcome in @entity1 with @entity360 and outperforms multiple logistic regression based models. These may be useful for risk stratification of @entity1 on presentation to the emergency department.
