title: The use of intelligent database systems in acute pancreatitis--a systematic review.
abstract: BACKGROUND
  Acute pancreatitis (AP) is a complex disease with multiple aetiological factors, wide ranging severity, and multiple challenges to effective triage and management. Databases, data mining and machine learning algorithms (MLAs), including artificial neural networks (ANNs), may assist by storing and interpreting data from multiple sources, potentially improving clinical decision-making.
  
  
  OBJECTIVE
  1) Identify database technologies used to store AP data, 2) collate and categorise variables stored in AP databases, 3) identify the MLA technologies, including ANNs, used to analyse AP data, and 4) identify clinical and non-clinical benefits and obstacles in establishing a national or international AP database.
  
  
  METHODS
  Comprehensive systematic search of online reference databases. The predetermined inclusion criteria were all papers discussing 1) databases, 2) data mining or 3) MLAs, pertaining to AP, independently assessed by two reviewers with conflicts resolved by a third author.
  
  
  RESULTS
  Forty-three papers were included. Three data mining technologies and five ANN methodologies were reported in the literature. There were 187 collected variables identified. ANNs increase accuracy of severity prediction, one study showed ANNs had a sensitivity of 0.89 and specificity of 0.96 six hours after admission--compare APACHE II (cutoff score ≥8) with 0.80 and 0.85 respectively. Problems with databases were incomplete data, lack of clinical data, diagnostic reliability and missing clinical data.
  
  
  CONCLUSIONS
  This is the first systematic review examining the use of databases, MLAs and ANNs in the management of AP. The clinical benefits these technologies have over current systems and other advantages to adopting them are identified.
authors.affiliation: Department of Surgery, University of Auckland, Auckland, New Zealand. Electronic address: m.vandenheever@auckland.ac.nz.
authors.forename: Marc
authors.initials: M
authors.lastname: van den Heever
authors.affiliation: Department of Surgery, University of Auckland, Auckland, New Zealand.
authors.forename: Anubhav
authors.initials: A
authors.lastname: Mittal
authors.affiliation: Department of Surgery, University of Auckland, Auckland, New Zealand.
authors.forename: Matthew
authors.initials: M
authors.lastname: Haydock
authors.affiliation: Department of Surgery, University of Auckland, Auckland, New Zealand. Electronic address: j.windsor@auckland.ac.nz.
authors.forename: John
authors.initials: J
authors.lastname: Windsor
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining; D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer; D010195:Pancreatitis / Q000175:diagnosis*
keywords: Acute pancreatitis; Artificial neural networks; Database management systems; Machine learning algorithms; Medical informatics