Abstract
There is an increasing shift towards the self-management of long-term chronic illness by patients in a home setting, supported by personal health electronic equipment. Among others, self-management requires comprehensive education on the illness, i.e., understanding the effects of nutritional, fitness, and medication choices on personal health; and long-term health behavior change, i.e., modifying unhealthy lifestyles that contribute to chronic illness. Smart health recommendations, generated using AI-based Clinical Decision Support (CDS), can guide patients towards positive nutritional, fitness, and health behavioral choices. Moreover, we posit that explaining these recommendations to patients, using Explainable AI (XAI) techniques, will effect education and positive behavior change. We present our work towards an explanation framework for rule-based CDS, called EXPLAIN (EXPLanations of AI In N3), which aims to generate human-readable, patient-facing explanations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Future work involves describing the underlying scientific explanation as well.
References
AndroJena. https://github.com/lencinhaus/androjena
Punya platform. http://punya.mit.edu/
Arndt, D., Van Woensel, W., Tomaszuk, D.: Notation3: draft community group report (2021). https://w3c.github.io/N3/spec/
Bandura, A.: Social cognitive theory of self-regulation. Organ. Behav. Hum. Decis. Process. 50(2), 248–287 (1991)
Berners-Lee, T., McCusker, J., Del Rio, N.: Provenance Markup Language (PML 3.0). https://github.com/timrdf/pml
Chari, S., Gruen, D.M., Seneviratne, O., McGuinness, D.L.: Foundations of explainable knowledge-enabled systems. In: Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges, Studies on the Semantic Web, vol. 47, pp. 23–48. IOS Press (2020). https://doi.org/10.3233/SSW200010
Chari, S., Seneviratne, O., Gruen, D.M., Foreman, M.A., Das, A.K., McGuinness, D.L.: Explanation ontology: a model of explanations for user-centered AI. In: Pan, J.Z., Tamma, V., d’Amato, C., Janowicz, K., Fu, B., Polleres, A., Seneviratne, O., Kagal, L. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 228–243. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_15
De Roo, J.: Euler Yet another proof Engine - EYE. https://josd.github.io/eye/
Peleg, M.: Computer-interpretable clinical guidelines: a methodological review. J. Biomed. Inf. 46(4), 744–763 (2013)
Rose-Davis, B., Van Woensel, W., Abidi, S.R., Stringer, E., Abidi, S.S.R.: Semantic knowledge modeling and evaluation of argument theory to develop dialogue based patient education systems for chronic disease self-management. Int. J. Med. Inf. 160, 104693 (2022)
Sanneman, L., Shah, J.A.: A situation awareness-based framework for design and evaluation of explainable AI. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds.) EXTRAAMAS 2020. LNCS (LNAI), vol. 12175, pp. 94–110. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51924-7_6
Shirai, S., Seneviratne, O., McGuinness, D.L.: Applying personal knowledge graphs to health. CoRR abs/2104.07587 (2021). https://arxiv.org/abs/2104.07587
van der Waa, J., Nieuwburg, E., Cremers, A., Neerincx, M.: Evaluating XAI: a comparison of rule-based and example-based explanations. Artif. Intell. 291, 103404 (2021)
Patton, E., Van Woensel, W., Seneviratne, O., Loseto, G., Scioscia, F., Kagal, L. Development of AI-enabled apps by patients and domain experts using the punya platform: a case study for diabetes. In: 20th International Conference on Artificial Intelligence in Medicine (2022)
Van Woensel, W.: AndroJena + PML. https://github.com/william-vw/androjena_jre
Van Woensel, W., Baig, W.H., Abidi, S.S.R., Abidi, S.R.: A semantic web framework for behavioral user modeling and action planning for personalized behavior modification. In: 10th International Conference on Semantic Web Applications and Tools for Life Sciences. CEUR, Rome, Italy (2017)
World health organization: innovative care for chronic conditions. Technical report (2002). https://www.who.int/chp/knowledge/publications/icccglobalreport.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Woensel, W.V. et al. (2022). Explainable Clinical Decision Support: Towards Patient-Facing Explanations for Education and Long-Term Behavior Change. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-09342-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09341-8
Online ISBN: 978-3-031-09342-5
eBook Packages: Computer ScienceComputer Science (R0)