Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis.

Objective: The aim of this study was to evaluate the efficacy of artificial neural networks (ANN) in predicting intra-abdominal infection in moderately severe (MASP) and severe acute pancreatitis (SAP) compared with that of a logistic regression model (LRM).

Methods: Patients suffering from MSAP or SAP from July 2014 to June 2017 in three affiliated hospitals of the Army Medical University in Chongqing, China, were enrolled in this study. A univariate analysis was used to determine the different parameters between patients with and without intra-abdominal infection.

Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.

Background: Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.

Methods: Patients with MSAP and SAP who were admitted from July 2014 to June 2017 were included.

[Classification technique for hyperspectral image based on subspace of bands feature extraction and LS-SVM].

The present paper proposes a novel hyperspectral image classification algorithm based on LS-SVM (least squares support vector machine). The LS-SVM uses the features extracted from subspace of bands (SOB). The maximum noise fraction (MNF) method is adopted as the feature extraction method.