Semantic Enrichment of Explanations of AI Models for Healthcare

Author:

Corbucci LucaORCID,Monreale AnnaORCID,Panigutti CeciliaORCID,Natilli MichelaORCID,Smiraglio Simona,Pedreschi DinoORCID

Publisher

Springer Nature Switzerland

Reference31 articles.

1. Aronson, A.R.: Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. In: Proceedings AMIA Symposium, pp. 17–21 (2001). ISSN 1531-605X. eprint 11825149. https://pubmed.ncbi.nlm.nih.gov/11825149

2. Barnett, A.J., et al.: IAIA-BL: a case-based interpretable deep learning model for classification of mass lesions in digital mammography. arXiv preprint arXiv:2103.12308 (2021)

3. Bhatt, U., et al.: Explainable machine learning in deployment. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 648–657 (2020)

4. Bodria, F., et al.: Benchmarking and survey of explanation methods for black box models. CoRR abs/2102.13076 (2021)

5. Boominathan, S., et al.: Treatment policy learning in multiobjective settings with fully observed outcomes. In: ACM SIGKDD 2020, pp. 1937–1947 (2020)

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