Artificial neural networks in pancreatic disease

Abstract

Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles.

The changing character of acute pancreatitis: Epidemiology, etiology, and prognosis

“…Some success has been achieved in determining the prognosis of patients with severe acute pancreatitis. Bartosch-Harlid et al [37] reviewed 11 reports and found that neural networks could be valuable for evaluating complex problems or for use when expert opinion is unavailable. Mofi di and colleagues [38] used artifi cial neural networks to identify severe disease in a group of 664 patients with acute pancreatitis.…”

Prediction of Severe Acute Pancreatitis at Admission to Hospital Using Artificial Neural Networks

“…Only a few studies have investigated ANNs in the prediction of outcome in AP, with varying outcome and degree of usefulness [31]. In 1998, Pofahl et al [15] published the first article using ANNs as a tool in prediction of severity of AP, where a hospital stay >7 days was used as a surrogate marker of severe disease.…”

Use of an Artificial Neural Network to Predict Risk Factors of Nosocomial Infection in Lung Cancer Patients

“…A systematic review suggested that ANN is potentially more successful than conventional statistical techniques at predicting clinical outcomes when the relationship between the variables that determine the prognosis is complex, multidimensional and non-linear (Bartosch-Harlid et al, 2008). The aim of this study was to develop an ANN to predict nosocomial infection in lung cancer.…”