title: Knowledge discovery in clinical decision support systems for pain management: a systematic review.
abstract: OBJECTIVE
  The occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain assessment and offering better support for clinical decision-making. This review is focused on computer technologies for pain management that allow CDSSs to obtain knowledge from the clinical data produced by either patients or health care professionals.
  
  
  METHODS
  A comprehensive literature search was conducted in several electronic databases to identify relevant articles focused on computerised systems that constituted CDSSs and include data or results related to pain symptoms from patients with acute or chronic pain, published between 1992 and 2011 in the English language. In total, thirty-nine studies were analysed; thirty-two were selected from 1245 citations, and seven were obtained from reference tracking.
  
  
  RESULTS
  The results highlighted the following clusters of computer technologies: rule-based algorithms, artificial neural networks, nonstandard set theory, and statistical learning algorithms. In addition, several methodologies were found for content processing such as terminologies, questionnaires, and scores. The median accuracy ranged from 53% to 87.5%.
  
  
  CONCLUSIONS
  Computer technologies that have been applied in CDSSs are important but not determinant in improving the systems' accuracy and the clinical practice, as evidenced by the moderate correlation among the studies. However, these systems play an important role in the design of computerised systems oriented to a patient's symptoms as is required for pain management. Several limitations related to CDSSs were observed: the lack of integration with mobile devices, the reduced use of web-based interfaces, and scarce capabilities for data to be inserted by patients.
authors.affiliation: Department of Informatics, University of Beira Interior, Rua Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal. Electronic address: ngpombo@ubi.pt.
authors.forename: Nuno
authors.initials: N
authors.lastname: Pombo
authors.affiliation: Instituto de Telecomunicações and Department of Informatics, University of Beira Interior, Rua Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal.
authors.forename: Pedro
authors.initials: P
authors.lastname: Araújo
authors.affiliation: Faculty of Health Sciences, University of Beira Interior, Av. Infante D. Henrique, 6200-506 Covilhã, Portugal.
authors.forename: Joaquim
authors.initials: J
authors.lastname: Viana
mesh_terms: D020000:Decision Support Systems, Clinical; D006801:Humans; D019359:Knowledge; D059408:Pain Management
keywords: Clinical decision support system; Machine learning; Pain measurement; Systematic review