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    <abstract>Extracting patient treatment timelines from clinical notes is a complex task involving identification of relevant events, temporal expressions, and temporal relations in individual documents and developing cross-document summaries. The 2025 Shared Task on Chemotherapy Treatment Timeline Extraction builds upon the initial 2024 challenge, using data from 57,530 breast and ovarian cancer patients and 15,946 melanoma patients. Participants were provided with a subset annotated for treatment entities, temporal expressions, temporal relations, and timelines for each patient. This training data was used to addressed two subtasks. Subtask 1 focused on extracting temporal relations and creating timelines, given documents and gold-standard events and temporal expressions. Sutask 2 involved development of an end-to-end system involving extraction of entities, temporal expressions, and relations, and construction of timelines, given only the Electronic Health Record notes. Five teams participated, submitting eight entries for Subtask 1 and twelve for Subtask 2. Supervised fine-tuning remains a productive approach albeit with a shift of supervised fine-tuning of very large language models compared to the 2024 task edition. Even with the much more “strict” evaluation metric, the best results are comparable to the best less strict 2024 relaxed-to-month results.</abstract>
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%0 Conference Proceedings
%T Overview of the 2025 Shared Task on Chemotherapy Treatment Timeline Extraction
%A Yao, Jiarui
%A Hochheiser, Harry
%A Yoon, WonJin
%A Goldner, Eli T.
%A Savova, Guergana K.
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 7th Clinical Natural Language Processing Workshop
%D 2025
%8 October
%I Association for Computational Linguistics
%C Virtual
%F yao-etal-2025-overview
%X Extracting patient treatment timelines from clinical notes is a complex task involving identification of relevant events, temporal expressions, and temporal relations in individual documents and developing cross-document summaries. The 2025 Shared Task on Chemotherapy Treatment Timeline Extraction builds upon the initial 2024 challenge, using data from 57,530 breast and ovarian cancer patients and 15,946 melanoma patients. Participants were provided with a subset annotated for treatment entities, temporal expressions, temporal relations, and timelines for each patient. This training data was used to addressed two subtasks. Subtask 1 focused on extracting temporal relations and creating timelines, given documents and gold-standard events and temporal expressions. Sutask 2 involved development of an end-to-end system involving extraction of entities, temporal expressions, and relations, and construction of timelines, given only the Electronic Health Record notes. Five teams participated, submitting eight entries for Subtask 1 and twelve for Subtask 2. Supervised fine-tuning remains a productive approach albeit with a shift of supervised fine-tuning of very large language models compared to the 2024 task edition. Even with the much more “strict” evaluation metric, the best results are comparable to the best less strict 2024 relaxed-to-month results.
%U https://aclanthology.org/2025.clinicalnlp-1.1/
%P 1-10

Markdown (Informal)

[Overview of the 2025 Shared Task on Chemotherapy Treatment Timeline Extraction](https://aclanthology.org/2025.clinicalnlp-1.1/) (Yao et al., ClinicalNLP 2025)

• Overview of the 2025 Shared Task on Chemotherapy Treatment Timeline Extraction (Yao et al., ClinicalNLP 2025)

ACL

• Jiarui Yao, Harry Hochheiser, WonJin Yoon, Eli T Goldner, and Guergana K Savova. 2025. Overview of the 2025 Shared Task on Chemotherapy Treatment Timeline Extraction. In Proceedings of the 7th Clinical Natural Language Processing Workshop, pages 1–10, Virtual. Association for Computational Linguistics.