Capturing Human Intelligence for Modelling Cognitive-Based Clinical Decision Support Agents

  • Ali Rezaei-YazdiEmail author
  • Christopher D. Buckingham
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 732)


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 (


Intelligent agents Clinical intelligence Clinical Decision Support Systems Dynamic data collection Risk assessment Healthcare eHealth 


  1. 1.
    Pryor, T.A.: Development of decision support systems. Int. J. Clin. Monit. Comput. 7(3), 137–146 (1990)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Campbell, H., Hotchkiss, R., Bradshaw, N., Porteous, M.: Integrated care pathways. BMJ Br. Med. J. 316(7125), 133 (1998)CrossRefGoogle Scholar
  3. 3.
    Fox, J., Patkar, V., Thomson, R.: Decision support for health care: the proforma evidence base. J. Innov. Health Inform. 14(1), 49–54 (2006)CrossRefGoogle Scholar
  4. 4.
    Kawamoto, K., Houlihan, C.A., Balas, E.A., Lobach, D.F.: Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 330(7494), 765 (2005)CrossRefGoogle Scholar
  5. 5.
    Sanchez, F.: Suicide Explained: A Neuropsychological Approach. Xlibris Corporation (2007)Google Scholar
  6. 6.
    Brunton, K.: The evidence on how nurses approach risk assessment. Nurs. Times 101(28), 38 (2005)Google Scholar
  7. 7.
    Kilsdonk, E., Peute, L.W., Riezebos, R.J., Kremer, L.C., Jaspers, M.W.M.: From an expert-driven paper guideline to a user-centred decision support system: a usability comparison study. Artif. Intell. Med. 59(1), 5–13 (2013)CrossRefGoogle Scholar
  8. 8.
    Peute, L.W.P., Jaspers, M.W.M.: The significance of a usability evaluation of an emerging laboratory order entry system. Int. J. Med. Inform. 76(2), 157–168 (2007)CrossRefGoogle Scholar
  9. 9.
    Sittig, D.F., Wright, A., Osheroff, J.A., Middleton, B., Teich, J.M., Ash, J.S., Campbell, E., Bates, D.W.: Grand challenges in clinical decision support. J. Biomed. Inform. 41(2), 387–392 (2008)CrossRefGoogle Scholar
  10. 10.
    Sampson, D.L., Parker, T.J., Upton, Z., Hurst, C.P.: A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches. PloS One 6(9), e24973 (2011)CrossRefGoogle Scholar
  11. 11.
    Dozois, D.J.A.: Psychological treatments: putting evidence into practice and practice into evidence. Can. Psychol./Psychologie canadienne 54(1), 1 (2013)CrossRefGoogle Scholar
  12. 12.
    Rezaei-Yazdi, A., Buckingham, C.D.: Understanding data collection behaviour of mental health practitioners. Stud. Health Technol. Inform. 207, 193–202 (2014)Google Scholar
  13. 13.
    GRiST: Galatean risk and safety tool. Accessed 15 May 2014
  14. 14.
    Buckingham, C.D.: Psychological cue use and implications for a clinical decision support system. Med. Inform. Internet Med. 27(4), 237–251 (2002)CrossRefGoogle Scholar
  15. 15.
    Byers, S.N., Roberts, C.A.: Bayes’ theorem in paleopathological diagnosis. Am. J. Phys. Anthropol. 121(1), 1–9 (2003)CrossRefGoogle Scholar
  16. 16.
    Spiegelhalter, D.J., Abrams, K.R., Myles, J.P.: Bayesian Approaches to Clinical Trials and Health-Care Evaluation, vol. 13. Wiley, Chichester (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Aston UniversityBirminghamUK

Personalised recommendations