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

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.

Publication DOI: https://doi.org/10.1016/j.procs.2016.08.237
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © 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/).
Uncontrolled Keywords: clinical decision support systems,dynamic data collection,healthcare,mental health,risk assessment,risk classification,Computer Science(all)
Publication ISSN: 1877-0509
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 0476?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2016
Published Online Date: 2016-09-04
Accepted Date: 2016-09-01
Authors: Rezaei-Yazdi, Ali
Buckingham, Christopher (ORCID Profile 0000-0002-3675-1215)

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