@inproceedings{jain-etal-2025-team,
title = "{TEAM} {UAB} at Chemotherapy Timelines 2025: Integrating Encoders and Large Language Models for Chemotherapy Timelines Generation",
author = "Jain, Vijay Raj and
Coffee, Chris and
He, Kaiwen and
Cron, Remy and
Cochran, Micah D. and
Mansilla-Gonzalez, Luis and
Nadimpalli, Akhil and
Murad, Danish and
Osborne, John D",
editor = "Ben Abacha, Asma and
Bethard, Steven and
Bitterman, Danielle and
Naumann, Tristan and
Roberts, Kirk",
booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
month = oct,
year = "2025",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clinicalnlp-1.5/",
pages = "30--39",
abstract = "Reconstructing the timeline of Systemic Anticancer Therapy (SACT) or ``chemotherapy'' from heterogeneous Electronic Health Record(EHR) notes is a challenging task. Rapid developments in Large Language Models (LLMs), including a range of architectural improvements and post-training refinements since the 2024 Chemotherapy Timelines Task could make this task more tractable. We evaluated the performance of 4 recently released LLMs (GPT-4.1-mini, Phi4 and 2 Qwen3 models) on this task. Our results indicate that even witha variety of prompt optimization and synthetic data training, more work is still needed to see a useful application of this work."
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<abstract>Reconstructing the timeline of Systemic Anticancer Therapy (SACT) or “chemotherapy” from heterogeneous Electronic Health Record(EHR) notes is a challenging task. Rapid developments in Large Language Models (LLMs), including a range of architectural improvements and post-training refinements since the 2024 Chemotherapy Timelines Task could make this task more tractable. We evaluated the performance of 4 recently released LLMs (GPT-4.1-mini, Phi4 and 2 Qwen3 models) on this task. Our results indicate that even witha variety of prompt optimization and synthetic data training, more work is still needed to see a useful application of this work.</abstract>
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%0 Conference Proceedings
%T TEAM UAB at Chemotherapy Timelines 2025: Integrating Encoders and Large Language Models for Chemotherapy Timelines Generation
%A Jain, Vijay Raj
%A Coffee, Chris
%A He, Kaiwen
%A Cron, Remy
%A Cochran, Micah D.
%A Mansilla-Gonzalez, Luis
%A Nadimpalli, Akhil
%A Murad, Danish
%A Osborne, John D.
%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 jain-etal-2025-team
%X Reconstructing the timeline of Systemic Anticancer Therapy (SACT) or “chemotherapy” from heterogeneous Electronic Health Record(EHR) notes is a challenging task. Rapid developments in Large Language Models (LLMs), including a range of architectural improvements and post-training refinements since the 2024 Chemotherapy Timelines Task could make this task more tractable. We evaluated the performance of 4 recently released LLMs (GPT-4.1-mini, Phi4 and 2 Qwen3 models) on this task. Our results indicate that even witha variety of prompt optimization and synthetic data training, more work is still needed to see a useful application of this work.
%U https://aclanthology.org/2025.clinicalnlp-1.5/
%P 30-39
Markdown (Informal)
[TEAM UAB at Chemotherapy Timelines 2025: Integrating Encoders and Large Language Models for Chemotherapy Timelines Generation](https://aclanthology.org/2025.clinicalnlp-1.5/) (Jain et al., ClinicalNLP 2025)
ACL
- Vijay Raj Jain, Chris Coffee, Kaiwen He, Remy Cron, Micah D. Cochran, Luis Mansilla-Gonzalez, Akhil Nadimpalli, Danish Murad, and John D Osborne. 2025. TEAM UAB at Chemotherapy Timelines 2025: Integrating Encoders and Large Language Models for Chemotherapy Timelines Generation. In Proceedings of the 7th Clinical Natural Language Processing Workshop, pages 30–39, Virtual. Association for Computational Linguistics.