Challenges in Explaining Pretrained Clinical Text Classifiers
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Computer Science > Computation and Language
Title:Challenges in Explaining Pretrained Clinical Text Classifiers
Abstract:Explaining the predictions of neural models in clinical NLP remains a significant challenge, especially for complex tasks involving long, unstructured medical texts. While post-hoc methods like LIME and SHAP are widely used, they often fall short when applied to clinical narratives. In this paper, we identify core limitations of token-level and perturbation-based explanation techniques through targeted demonstra- tions on a hospital length-of-stay prediction task. Our findings reveal issues such as overemphasis on non-informative tokens, instability in at- tributions, and high-confidence predictions for incoherent input variants. These results underscore the need for explanation strategies that are clin- ically meaningful, semantically grounded, and robust to linguistic noise.
| Comments: | 9 pages, 7 figures. Accepted at the First Workshop on Responsible Healthcare using Machine Learning (RHCML 2025), co-located with ECML PKDD 2025 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.28060 [cs.CL] |
| (or arXiv:2605.28060v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28060
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2025. Communications in Computer and Information Science, vol 2842, pp. 314-322. Springer, Cham (2026) |
| Related DOI: | https://doi.org/10.1007/978-3-032-19105-2_22
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