Introducing a pilot data collection model for real-time evaluation of data redundancy

Ali Rezaei-Yazdi*, Christopher Buckingham

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage.

Original languageEnglish
Pages (from-to)577-586
Number of pages10
JournalProcedia Computer Science
Volume96
Early online date4 Sep 2016
DOIs
Publication statusPublished - 2016
Event20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems - York, United Kingdom
Duration: 5 Sep 20167 Sep 2016

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Bibliographical note

© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords

  • clinical decision support systems
  • dynamic data collection
  • healthcare
  • mental health
  • risk assessment
  • risk classification

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