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

Journal: Journal of digestive diseases
PMID: 31328389

Abstract

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).

Authors

  • Qiu Qiu
    Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
  • Yong Jian Nian
    Department of Medical Images, College of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
  • Liang Tang
    Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
  • Yan Guo
    State Key Laboratory of Pathogen and Biosecurity, Beijing 100071, China.
  • Liang Zhi Wen
    Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Dong Feng Chen
    Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
  • Kai Jun Liu
    Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.

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