Integrating Symbolic Knowledge and Machine Learning in Healthcare
Sirocchi C, Montagna S (2024)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2024
Publisher: CEUR Workshop Proceedings
Series: RuleML+RR-Companion 2024 Rule Challenge, Doctoral Consortium, Industry Track, and Networking Session 2024
City/Town: Aachen
Book Volume: 3816
Conference Proceedings Title: Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning co-located with 20th Reasoning Web Summer School (RW 2024) as part of Declarative AI 2024
Event location: Bucharest
URI: https://ceur-ws.org/Vol-3816/paper45.pdf
Open Access Link: https://ceur-ws.org/Vol-3816/paper45.pdf
Abstract
The intersection of Artificial Intelligence and healthcare has driven advancements, particularly through
machine learning, which exploits large datasets to develop predictive models and identify risk factors.
Despite its success in clinical medicine, only a few models are FDA-approved due to issues of trustworthiness and lack of explainability, hindering adoption in clinical settings. Addressing these issues, symbolic
knowledge injection and symbolic knowledge extraction have emerged. The first approach integrates
domain-specific expertise encoded as rules into machine learning models, while the second extracts
interpretable rules from trained models.
In this study, this framework is validated using the Pima Indians diabetes dataset, a benchmark in
diabetes research. By incorporating a diagnostic protocol for diabetes into machine learning models,
the study demonstrates an improvement in the predictive capabilities of these models. By extracting
rules from pure data-driven trained models and integrating them with medical knowledge, we reduce
false negatives, while achieving a fully explainable diagnostic system. Finally, a combination of these
two methods is explored, reporting higher diabetes detection rates and improved model explainability.
Accordingly, this study demonstrates the potential of combining machine-learnt insights with medical
guidelines to improve healthcare outcomes.
Authors with CRIS profile
Involved external institutions
Italy (IT)How to cite
APA:
Sirocchi, C., & Montagna, S. (2024). Integrating Symbolic Knowledge and Machine Learning in Healthcare. In Anisa Rula, Emanuel Sallinger, Ognjen Savkovic, Ioana Georgiana Ciuciu, Ioan Toma, Josiane Xavier Parreira, Radu Prodan, Hui Song, Ahmet Soylu (Eds.), Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning co-located with 20th Reasoning Web Summer School (RW 2024) as part of Declarative AI 2024. Bucharest, RO: Aachen: CEUR Workshop Proceedings.
MLA:
Sirocchi, Christel, and Sara Montagna. "Integrating Symbolic Knowledge and Machine Learning in Healthcare." Proceedings of the 8th International Joint Conference on Rules and Reasoning, Bucharest Ed. Anisa Rula, Emanuel Sallinger, Ognjen Savkovic, Ioana Georgiana Ciuciu, Ioan Toma, Josiane Xavier Parreira, Radu Prodan, Hui Song, Ahmet Soylu, Aachen: CEUR Workshop Proceedings, 2024.
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