@inproceedings{alliheedi-etal-2026-zero,
title = "Zero-Shot, Fine-Tuned, and Retrieval-Augmented Extraction of Clinical Decisions with Corpus Boundary Diagnostics",
author = "Alliheedi, Mohammed and
Mercer, Robert and
Machina, Anemily and
Roy, Sudipta and
Wang, Yetian and
Wang, Xindi",
editor = "Gupta, Deepak and
Demner-Fushman, Dina",
booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.bionlp-2.20/",
pages = "141--145",
ISBN = "979-8-89176-435-4",
abstract = "We present the CanSA system for the MedEx-ACT@ACL 2026 shared task, which requires extracting and classifying clinical decisions from ICU discharge summaries into nine DIC-TUM categories. We have developed three approaches: (1) a training-free system which consists of a preprocessing module that normalizes text and an inference engine combining zero shot LLMs with a RAG ensemble, (2) a supervised fine-tuning method which required training, and (3) a training-free retrieval-augmented pipeline employing TF{--}IDF-based lexical retrieval to surface in-context exemplars from the development corpus, combined with section aware chunking and structured extraction calls to a large language model. Our team{'}s best submission achieved a Final Score of 0.41, ranking 34th out of 37 on the official test leaderboard."
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<abstract>We present the CanSA system for the MedEx-ACT@ACL 2026 shared task, which requires extracting and classifying clinical decisions from ICU discharge summaries into nine DIC-TUM categories. We have developed three approaches: (1) a training-free system which consists of a preprocessing module that normalizes text and an inference engine combining zero shot LLMs with a RAG ensemble, (2) a supervised fine-tuning method which required training, and (3) a training-free retrieval-augmented pipeline employing TF–IDF-based lexical retrieval to surface in-context exemplars from the development corpus, combined with section aware chunking and structured extraction calls to a large language model. Our team’s best submission achieved a Final Score of 0.41, ranking 34th out of 37 on the official test leaderboard.</abstract>
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%0 Conference Proceedings
%T Zero-Shot, Fine-Tuned, and Retrieval-Augmented Extraction of Clinical Decisions with Corpus Boundary Diagnostics
%A Alliheedi, Mohammed
%A Mercer, Robert
%A Machina, Anemily
%A Roy, Sudipta
%A Wang, Yetian
%A Wang, Xindi
%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 alliheedi-etal-2026-zero
%X We present the CanSA system for the MedEx-ACT@ACL 2026 shared task, which requires extracting and classifying clinical decisions from ICU discharge summaries into nine DIC-TUM categories. We have developed three approaches: (1) a training-free system which consists of a preprocessing module that normalizes text and an inference engine combining zero shot LLMs with a RAG ensemble, (2) a supervised fine-tuning method which required training, and (3) a training-free retrieval-augmented pipeline employing TF–IDF-based lexical retrieval to surface in-context exemplars from the development corpus, combined with section aware chunking and structured extraction calls to a large language model. Our team’s best submission achieved a Final Score of 0.41, ranking 34th out of 37 on the official test leaderboard.
%U https://aclanthology.org/2026.bionlp-2.20/
%P 141-145
Markdown (Informal)
[Zero-Shot, Fine-Tuned, and Retrieval-Augmented Extraction of Clinical Decisions with Corpus Boundary Diagnostics](https://aclanthology.org/2026.bionlp-2.20/) (Alliheedi et al., BioNLP 2026)
ACL
- Mohammed Alliheedi, Robert Mercer, Anemily Machina, Sudipta Roy, Yetian Wang, and Xindi Wang. 2026. Zero-Shot, Fine-Tuned, and Retrieval-Augmented Extraction of Clinical Decisions with Corpus Boundary Diagnostics. In Proceedings of the BioNLP 2026 (Shared Tasks), pages 141–145, San Diego, California, USA. Association for Computational Linguistics.