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Artificial Neural Networks for Early Prediction of Mortality in Patients with Non Variceal Upper GI Bleeding (UGIB)

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Author(s): Enzo Grossi | Riccardo Marmo | Marco Intraligi | Massimo Buscema

Journal: Biomedical Informatics Insights
ISSN 1178-2226

Volume: 1;
Start page: 7;
Date: 2008;
Original page

Keywords: upper gastrointestinal haemorrhage | non variceal bleeding | 30-day mortality | clinical outcomes | endoscopic therapy | prognostic factors | artificial neural networks

ABSTRACT
Background: Mortality for non variceal upper gastrointestinal bleeding (UGIB) is clinically relevant in the first 12–24 hours of the onset of haemorrhage and therefore identification of clinical factors predictive of the risk of death before endoscopic examination may allow for early corrective therapeutic intervention.Aim: 1) Identify simple and early clinical variables predictive of the risk of death in patients with non variceal UGIB; 2) assess previsional gain of a predictive model developed with conventional statistics vs. that developed with artificial neural networks (ANNs).Methods and results: Analysis was performed on 807 patients with nonvariceal UGIB (527 males, 280 females), as a part of a multicentre Italian study. The mortality was considered “bleeding-related” if occurred within 30 days from the index bleeding episode. A total of 50 independent variables were analysed, 49 of which clinico-anamnestic, all collected prior to endoscopic examination plus the haemoglobin value measured on admission in the emergency department. Death occurred in 42 (5.2%). Conventional statistical techniques (linear discriminant analysis) were compared with ANNs (Twist® system-Semeion) adopting the same result validation protocol with random allocation of the sample in training and testing subsets and subsequent cross-over. ANNs resulted to be significantly more accurate than LDA with an overall accuracy rate near to 90%.Conclusion: Artificial neural networks technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of death for UGIB.

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