Abstract
Clinical practice guidelines (CPGs) suffer from several limitations, including limited patient participation. APPRAISE-RS is a methodology for generating treatment recommendations that overcomes this limitation by enabling both patients and clinicians to express their personal preferences about the treatment outcomes. However, patient, and clinical preferences are treated with equal importance, while it seems reasonable/fair to give more importance to clinicians’ preferences as they have more experience on the matter. In this work we present APPRAISE-RS-E, which considers different ponderations when including users’ preferences based on their experience for the generation of treatment recommendations. Moreover, since users are involved in the decision loop, an explanation of the recommendations is provided. Finally, as APPRAISE-RS-E uses AI methods, it has been evaluated using a set of principles and observable indicators, getting an ethical seal that informs users about the ethical issues involved. The experiments have been carried out in the field of attention deficit hyperactivity disorder (ADHD).
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
References
Alonso-Coello, P., Oxman, A., et al.: GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 2: Clinical practice guidelines. BMJ (Clin. Res. ed.) 353, BMJ.I2089 (2016). https://doi.org/10.1136/BMJ.I2089
Arts, D., Voncken, A., et al.: Reasons for intentional guideline non-adherence: a systematic review. Int. J. Med. Inf. 89, 55–62 (2016). https://doi.org/10.1016/J.IJMEDINF.2016.02.009
Cabral, J.B., Luczywo, N.A., Zanazzi, J.L.: cikit-criteria: Colección de métodos de análisis multi-criterio integrado al stack científico de Python. In: 45JAIIO, pp. 59–66 (2016). http://45jaiio.sadio.org.ar/sites/default/files/Sio-23.pdf
Kim, C., Armstrong, M.J., et al.: How to identify, incorporate and report patient preferences in clinical guidelines: a scoping review. Health Expect. 23(5), 1028–1036 (2020). https://doi.org/10.1111/hex.13099
López, B., Raya, O., et al.: APPRAISE-RS: automated, updated, participatory, and personalized treatment recommender systems based on grade methodology. Heliyon (2023). https://doi.org/10.1016/j.heliyon.2023.e13074
López, B., Torrent-Fontbona, F., et al.: HTE 3.0: Knowledge-based systems in cascade for familial hypercholesterolemia detection and dyslipidemia treatment. Exp. Syst. 39, e12835 2022). https://doi.org/10.1111/EXSY.12835
OEIAC: Pio self assessment model (2023). https://oeiac.cat/en/pio-self-assessment-model/. Accessed 16 Mar 2023
Torra, V., Narukawa, Y.: Modeling decisions : information fusion and aggregation operators. Springer (2010). https://doi.org/10.1007/978-3-540-68791-7
Acknowledgements
This work received joint funding from the European Regional Development Fund (ERDF), the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO), the Carlos III Research Institute (grant no. PI19/00375), and received support from the Generalitat de Catalunya 2021 SGR 01125.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Raya, O., Castells, X., Ramírez, D., López, B. (2023). Management of Patient and Physician Preferences and Explanations for Participatory Evaluation of Treatment with an Ethical Seal. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_47
Download citation
DOI: https://doi.org/10.1007/978-3-031-34344-5_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34343-8
Online ISBN: 978-3-031-34344-5
eBook Packages: Computer ScienceComputer Science (R0)