Literature DB >> 24370382

Knowledge discovery in clinical decision support systems for pain management: a systematic review.

Nuno Pombo1, Pedro Araújo2, Joaquim Viana3.   

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 AND MATERIALS: 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.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Clinical decision support system; Machine learning; Pain measurement; Systematic review

Mesh:

Year:  2013        PMID: 24370382     DOI: 10.1016/j.artmed.2013.11.005

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  4 in total

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Authors:  Douglas P Gross; Susan Armijo-Olivo; William S Shaw; Kelly Williams-Whitt; Nicola T Shaw; Jan Hartvigsen; Ziling Qin; Christine Ha; Linda J Woodhouse; Ivan A Steenstra
Journal:  J Occup Rehabil       Date:  2016-09

3.  The relative meaning of absolute numbers: the case of pain intensity scores as decision support systems for pain management of patients with dementia.

Authors:  Valentina Lichtner; Dawn Dowding; S José Closs
Journal:  BMC Med Inform Decis Mak       Date:  2015-12-24       Impact factor: 2.796

4.  Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning.

Authors:  Anne Estrup Olesen; Debbie Grønlund; Mikkel Gram; Frank Skorpen; Asbjørn Mohr Drewes; Pål Klepstad
Journal:  BMC Res Notes       Date:  2018-01-27
  4 in total

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