Capturing human intelligence for modelling cognitive-based clinical decision support agents

Abstract

The success of intelligent agents in clinical care depends on the degree to which they represent and work with human decision makers. This is particularly important in the domain of clinical risk assessment where such agents either conduct the task of risk evaluation or support human clinicians with the task. This paper provides insights into how to understand and capture the cognitive processes used by clinicians when collecting the most important data about a person’s risks. It attempts to create some theoretical foundations for developing clinically justifiable and reliable decision support systems for initial risk screening. The idea is to direct an assessor to the most informative next question depending on what has already been asked using a mixture of probabilities and heuristics. The method was tested on anonymous mental health data collected by the GRiST risk and safety tool (www.egrist.org).

Publication DOI: https://doi.org/10.1007/978-3-319-90418-4_9
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: Copyright:Springer
Event Title: 2nd International Symposium on Artificial Life and Intelligent Agents, ALIA 2016
Event Type: Other
Event Dates: 2016-06-14 - 2016-06-15
Uncontrolled Keywords: Clinical decision support systems,Clinical intelligence,Dynamic data collection,eHealth,Healthcare,Intelligent agents,Risk assessment,Computer Science(all),Mathematics(all)
ISBN: 9783319904177
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 3-319-90418-4_9 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2018-04-19
Published Online Date: 2018-04-19
Accepted Date: 2018-04-01
Authors: Rezaei-Yazdi, Ali
Buckingham, Christopher D. (ORCID Profile 0000-0002-3675-1215)

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