Joakim Edin, Andreas Geert Motzfeldt, Casper L. Christensen, Tuukka Ruotsalo, Lars Maaløe, Maria Maistro. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes.Language models have significantly enhanced the processing of such records, streamlining workflows and reducing manual data entry, thereby saving healthcare providers significant resources.However, the black-box nature of these models often leaves healthcare professionals hesitant to trust them.State-ofthe-art explainability methods increase model transparency but rely on human-annotated evidence spans, which are costly.In this study, we propose an approach to produce plausible and faithful explanations without needing such annotations.We demonstrate on the automated medical coding task that adversarial robustness training improves explanation plausibility and introduce AttInGrad, a new explanation method superior to previous ones.By combining both contributions in a fully unsupervised setup, we produce explanations of comparable quality, or better, to that of a supervised approach.We release our code and model weights. 1

Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. In previous work, the macro F1 score has been calculated sub-optimally, and our correction doubles it. We contribute a revised model comparison using stratified sampling and identical experimental setups, including hyperparameters and decision boundary tuning. We analyze prediction errors to validate and falsify assumptions of previous works. The analysis confirms that all models struggle with rare codes, while long documents only have a negligible impact. Finally, we present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models. We release our code, model parameters, and new MIMIC-III and MIMIC-IV training and evaluation pipelines to accommodate fair future comparisons.

Jakob D. Havtorn, Jan Latko, Joakim Edin, Lars Maaløe, Lasse Borgholt, Lorenzo Belgrano, Nicolai Jacobsen, Regitze Sdun, Željko Agić. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.