Abstract
Adopting Clinical Decision Support Systems (CDSS) in clinical practice has shown to benefit both patients and healthcare providers. These CDSS need to be updated when new evidence, data, or guidelines arise since up-to-date evidence directly impacts physician acceptance and adherence to these systems. To this end, in previous studies, methodologies have been developed to update CDSS content by taking advantage of machine learning (ML) algorithms. Modifications in the domain knowledge require a reviewing and validation process before being implemented in clinical practice. Hence, this paper presents a methodology for including real-world evidence in an evidence-based CDSS for breast cancer use case. Decision trees (DT) algorithms are used to suggest modifications based on the analysis of retrospective data, which clinical experts review before being implanted in the CDSS. This way, our methodology allows to combine clinical knowledge from both guidelines and real-world data and enrich the domain clinical knowledge with real-world evidence.
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References
S Berner, E.: Clinical decision support systems: theory and practice. Springer Berlin Heidelberg, New York, NY (2016) https://doi.org/10.1007/978-3-319-31913-1
Cabana, M.D., et al.: Why Don’t Physicians Follow Clinical Practice Guidelines?A Framework for Improvement. JAMA 282, 1458–1465 (1999)
Davis, D.A., Taylor-Vaisey, A.: Translating guidelines into practice: a systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CMAJ 157, 408–416 (1997)
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., Cilar, L.: Interpretability of machine learning-based prediction models in healthcare. WIREs Data Min. Knowl. Discovery 10, e1379 (2020)
Petch, J., Di, S., Nelson, W.: Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology. Can. J. Cardiol. 38, 204–213 (2022)
Sutton, R.T., Pincock, D., Baumgart, D.C., Sadowski, D.C., Fedorak, R.N., Kroeker, K.I.: An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digit. Med. 3, 1–10 (2020)
Bai, Y., et al.: Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure. Front Pharmacol. 12, 758573 (2022)
Zhao, W., Jiang, X., Wang, K., Sun, X., Hu, G., Xie, G.: Construction of Guideline-Based Decision Tree for Medication Recommendation. Stud Health Technol Inform. (2020)
Torres, J., Artola, G., Muro, N.: A Domain-Independent Semantically Validated Authoring Tool for Formalizing Clinical Practice Guidelines. Stud Health Technol Inform. 270, 517–521 (2020)
Acknowledgements
The authors want to thank Bilbomática and Magda Palka-Kotlowska (from Ribera Salud) for the provided aid during the formalization of the CPG used in the presented use cases, as well as for providing the clinical scenario with which to present the developed methodology.
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Torres, J., Alonso, E., Larburu, N. (2023). Real-World Evidence Inclusion in Guideline-Based Clinical Decision Support Systems: Breast Cancer Use Case. 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_43
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DOI: https://doi.org/10.1007/978-3-031-34344-5_43
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