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            <title>Proceedings of the BioNLP 2026 (Shared Tasks)</title>
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            <namePart type="given">Deepak</namePart>
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    <abstract>Extracting structured medical decisions fromICU discharge summaries is hard because oflong documents, severe category imbalanceacross nine DICTUM decision types, and afairness-aware evaluation that penalizes incon-sistent performance across demographic sub-groups. We present our system for the MedEx-ACT 2026 shared task (Elgaar et al., 2026),which fine-tunes BiomedBERT with a com-posite loss combining label-smoothed cross-entropy, a soft token-F1 auxiliary term, andR-Drop regularization. At inference time weapply a deterministic ensemble: half-offsetsliding-window augmentation across four win-dow configurations, dual-branch logit aggrega-tion from the same checkpoint, per-categorylength calibration on the Anchor Branch, andsparse routing of categories 4 and 7 to a context-weighted specialist branch motivated by theirunusual span-length distributions. Adding R-Drop improved validation Overall_F1 by 1.24points over the CE + soft-F1 baseline, with alarger 1.70-point gain on Worst-Group F1. Ourbest submission achieves Span F1 of 0.4900,Token F1 of 0.6796, and an official Overall_F1of 0.5724, with the African American subgroupas the Worst-Group bottleneck at Base_Score0.5601</abstract>
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%0 Conference Proceedings
%T Sparse Category Routing and Fairness-Aware Optimization for Medical Decision Extraction
%A Elshehaby, Ahmed
%A Abdalla, Mohamed
%A Mohamed, Youssef
%Y Gupta, Deepak
%Y Demner-Fushman, Dina
%S Proceedings of the BioNLP 2026 (Shared Tasks)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-435-4
%F elshehaby-etal-2026-sparse
%X Extracting structured medical decisions fromICU discharge summaries is hard because oflong documents, severe category imbalanceacross nine DICTUM decision types, and afairness-aware evaluation that penalizes incon-sistent performance across demographic sub-groups. We present our system for the MedEx-ACT 2026 shared task (Elgaar et al., 2026),which fine-tunes BiomedBERT with a com-posite loss combining label-smoothed cross-entropy, a soft token-F1 auxiliary term, andR-Drop regularization. At inference time weapply a deterministic ensemble: half-offsetsliding-window augmentation across four win-dow configurations, dual-branch logit aggrega-tion from the same checkpoint, per-categorylength calibration on the Anchor Branch, andsparse routing of categories 4 and 7 to a context-weighted specialist branch motivated by theirunusual span-length distributions. Adding R-Drop improved validation Overall_F1 by 1.24points over the CE + soft-F1 baseline, with alarger 1.70-point gain on Worst-Group F1. Ourbest submission achieves Span F1 of 0.4900,Token F1 of 0.6796, and an official Overall_F1of 0.5724, with the African American subgroupas the Worst-Group bottleneck at Base_Score0.5601
%U https://aclanthology.org/2026.bionlp-2.27/
%P 201-212

Markdown (Informal)

[Sparse Category Routing and Fairness-Aware Optimization for Medical Decision Extraction](https://aclanthology.org/2026.bionlp-2.27/) (Elshehaby et al., BioNLP 2026)

• Sparse Category Routing and Fairness-Aware Optimization for Medical Decision Extraction (Elshehaby et al., BioNLP 2026)

ACL

• Ahmed Elshehaby, Mohamed Abdalla, and Youssef Mohamed. 2026. Sparse Category Routing and Fairness-Aware Optimization for Medical Decision Extraction. In Proceedings of the BioNLP 2026 (Shared Tasks), pages 201–212, San Diego, California, USA. Association for Computational Linguistics.