BACKGROUND: Models based on artificial @entity1274 (ANN) are useful in predicting outcome of @entity74 . There is currently no useful predictive model for risk assessment in acute @entity360 . We investigated whether ANN models using information available during triage could predict clinical outcome in @entity1 with this disorder. METHODS: ANN and multiple-logistic-regression (MLR) models were constructed from non-endoscopic data of @entity1 admitted with acute @entity360 . The performance of ANN in classifying @entity1 into high-risk and low-risk groups was compared with that of another validated scoring system (BLEED), with the outcome variables recurrent @entity548 , @entity204 , and therapeutic interventions for control of @entity548 . The ANN models were trained with data from @entity1 admitted to the primary institution during the first 12 months (n=120) and then internally validated with data from @entity1 admitted to the same institution during the next 6 months (n=70). The ANN models were then externally validated and direct comparison made with MLR in @entity1 admitted to an independent institution in another US state (n=142). FINDINGS: Clinical features were similar for training and validation groups. The predictive accuracy of ANN was significantly better than that of BLEED (predictive accuracy in internal validation group for @entity204 87% vs 21%; for recurrent @entity548 89% vs 41%; and for intervention 96% vs 46%) and similar to MLR. During external validation, ANN performed well in predicting @entity204 (97%), recurrent @entity548 (93%), and need for intervention (94%), and it was superior to MLR (70%, 73%, and 70%, respectively). INTERPRETATION: ANN can accurately predict the outcome for @entity1 presenting with acute @entity360 and may be generally useful for the risk stratification of these @entity1 .

Prediction of outcome in acute XXXX based on an artificial neural network: internal and external validation of a predictive model.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity548 :: ('MESH:D006470', 'Disease') :: ['bleeding', 'haemorrhage'] @entity1274 :: ('MESH:D006319', 'Disease') :: ['neural networks'] @entity74 :: ('MESH:D030342', 'Disease') :: ['various disorders'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death'] @entity360 :: ('MESH:D006471', 'Disease') :: ['lower-gastrointestinal haemorrhage']
@entity360 :: (MESH:D006471,Disease) :: ['lower-gastrointestinal haemorrhage']
