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

Rezaei-Yazdi, Ali and Buckingham, Christopher D. (2018). Capturing human intelligence for modelling cognitive-based clinical decision support agents. IN: Artificial Life and Intelligent Agents - Second International Symposium, ALIA 2016, Revised Selected Papers. Communications in Computer and Information Science, 732 . GBR: Springer.


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 (

Publication DOI:
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied 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. ( 0000-0002-3675-1215)



Version: Accepted Version

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