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An Annotated Corpus of Textual Explanations for Clinical Decision Support

Roland Roller; Aljoscha Burchardt; Nils Feldhus; Laura Seiffe; Klemens Budde; Simon Ronicke; Bilgin Osmanodja
In: Nicoletta Calzolari; Frederic Bechet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Jan Odijk; Stelios Piperidis (Hrsg.). Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022). International Conference on Language Resources and Evaluation (LREC), Marseille, France, Pages 2317-2326, ISBN 979-10-95546-72-6, European Language Resources Association (ELRA), 2022.


In recent years, machine learning for clinical decision support has gained more and more attention. In order to introduce such applications into clinical practice, a good performance might be essential, however, the aspect of trust should not be underestimated. For the treating physician using such a system and being (legally) responsible for the decision made, it is particularly important to understand the system’s recommendation. To provide insights into a model’s decision, various techniques from the field of explainability (XAI) have been proposed whose output is often enough not targeted to the domain experts that want to use the model. To close this gap, in this work, we explore how explanations could possibly look like in future. To this end, this work presents a dataset of textual explanations in context of decision support. Within a reader study, human physicians estimated the likelihood of possible negative patient outcomes in the near future and justified each decision with a few sentences. Using those sentences, we created a novel corpus, annotated with different semantic layers. Moreover, we provide an analysis of how those explanations are constructed, and how they change depending on physician, on the estimated risk and also in comparison to an automatic clinical decision support system with feature importance.


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