@inproceedings{elgaar-etal-2026-overview,
title = "Overview of the Medical Decision Extraction, Analysis, and Classification Task ({M}ed{E}x{ACT}) of {B}io{NLP} 2026",
author = "Elgaar, Mohamed and
Cheng, Jiali and
Vakil, Nidhi and
Sadrolashrafi, Mehrnaz and
Mohtarami, Mitra and
Wong, Adrian and
Amiri, Hadi and
Celi, Leo",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2026",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.bionlp-1.63/",
pages = "760--769",
ISBN = "979-8-89176-434-7",
abstract = "This paper presents an overview of the Medical Decision Extraction, Analysis, and Classification task (MedExACT) of BioNLP 2026. The focus of this task is the extraction and labeling of medical decisions in ICU discharge summaries. The task is built on MedDec, a MIMIC-III-based dataset of 451 expert-annotated summaries, and asks systems to extract and classify spans of text that contain medical decisions according to the decision categories defined in the Decision Identification and Classification Taxonomy for Use in Medicine (DICTUM). The official ranking combines span F1 and token F1 with a worst-group robustness metric computed over sex, race, and English-proficiency subgroups. MedExACT attracted broad international interest, with 130 official submissions from 36 teams comprising about 60?100 participants, and has improved information extraction performance by nearly 15{\%} over the previous state of the art. The submitted systems predominantly use long-context encoder models, ensemble decoding, boundary-refinement modules, and robustness-aware training or model selection, with the best submitted run reaching a final fairness-based F1 of 0.596."
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<abstract>This paper presents an overview of the Medical Decision Extraction, Analysis, and Classification task (MedExACT) of BioNLP 2026. The focus of this task is the extraction and labeling of medical decisions in ICU discharge summaries. The task is built on MedDec, a MIMIC-III-based dataset of 451 expert-annotated summaries, and asks systems to extract and classify spans of text that contain medical decisions according to the decision categories defined in the Decision Identification and Classification Taxonomy for Use in Medicine (DICTUM). The official ranking combines span F1 and token F1 with a worst-group robustness metric computed over sex, race, and English-proficiency subgroups. MedExACT attracted broad international interest, with 130 official submissions from 36 teams comprising about 60?100 participants, and has improved information extraction performance by nearly 15% over the previous state of the art. The submitted systems predominantly use long-context encoder models, ensemble decoding, boundary-refinement modules, and robustness-aware training or model selection, with the best submitted run reaching a final fairness-based F1 of 0.596.</abstract>
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%0 Conference Proceedings
%T Overview of the Medical Decision Extraction, Analysis, and Classification Task (MedExACT) of BioNLP 2026
%A Elgaar, Mohamed
%A Cheng, Jiali
%A Vakil, Nidhi
%A Sadrolashrafi, Mehrnaz
%A Mohtarami, Mitra
%A Wong, Adrian
%A Amiri, Hadi
%A Celi, Leo
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S BioNLP 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-434-7
%F elgaar-etal-2026-overview
%X This paper presents an overview of the Medical Decision Extraction, Analysis, and Classification task (MedExACT) of BioNLP 2026. The focus of this task is the extraction and labeling of medical decisions in ICU discharge summaries. The task is built on MedDec, a MIMIC-III-based dataset of 451 expert-annotated summaries, and asks systems to extract and classify spans of text that contain medical decisions according to the decision categories defined in the Decision Identification and Classification Taxonomy for Use in Medicine (DICTUM). The official ranking combines span F1 and token F1 with a worst-group robustness metric computed over sex, race, and English-proficiency subgroups. MedExACT attracted broad international interest, with 130 official submissions from 36 teams comprising about 60?100 participants, and has improved information extraction performance by nearly 15% over the previous state of the art. The submitted systems predominantly use long-context encoder models, ensemble decoding, boundary-refinement modules, and robustness-aware training or model selection, with the best submitted run reaching a final fairness-based F1 of 0.596.
%U https://aclanthology.org/2026.bionlp-1.63/
%P 760-769
Markdown (Informal)
[Overview of the Medical Decision Extraction, Analysis, and Classification Task (MedExACT) of BioNLP 2026](https://aclanthology.org/2026.bionlp-1.63/) (Elgaar et al., BioNLP 2026)
ACL
- Mohamed Elgaar, Jiali Cheng, Nidhi Vakil, Mehrnaz Sadrolashrafi, Mitra Mohtarami, Adrian Wong, Hadi Amiri, and Leo Celi. 2026. Overview of the Medical Decision Extraction, Analysis, and Classification Task (MedExACT) of BioNLP 2026. In BioNLP 2026, pages 760–769, San Diego, California. Association for Computational Linguistics.