@inproceedings{haddadan-etal-2024-lailab,
title = "{LAIL}ab at Chemotimelines 2024: Finetuning sequence-to-sequence language models for temporal relation extraction towards cancer patient undergoing chemotherapy treatment",
author = "Haddadan, Shohreh and
Le, Tuan-Dung and
Duong, Thanh and
Thieu, Thanh",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Bitterman, Danielle",
booktitle = "Proceedings of the 6th Clinical Natural Language Processing Workshop",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clinicalnlp-1.37",
doi = "10.18653/v1/2024.clinicalnlp-1.37",
pages = "382--393",
abstract = "In this paper, we report our effort to tackle the challenge of extracting chemotimelines from EHR notes across a dataset of three cancer types. We focus on the two subtasks: 1) detection and classification of temporal relations given the annotated chemotherapy events and time expressions and 2) directly extracting patient chemotherapy timelines from EHR notes. We address both subtasks using Large Language Models. Our best-performing methods in both subtasks use Flan-T5, an instruction-tuned language model. Our proposed system achieves the highest average score in both subtasks. Our results underscore the effectiveness of finetuning general-domain large language models in domain-specific and unseen tasks.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="haddadan-etal-2024-lailab">
<titleInfo>
<title>LAILab at Chemotimelines 2024: Finetuning sequence-to-sequence language models for temporal relation extraction towards cancer patient undergoing chemotherapy treatment</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shohreh</namePart>
<namePart type="family">Haddadan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tuan-Dung</namePart>
<namePart type="family">Le</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thanh</namePart>
<namePart type="family">Duong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thanh</namePart>
<namePart type="family">Thieu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 6th Clinical Natural Language Processing Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tristan</namePart>
<namePart type="family">Naumann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Asma</namePart>
<namePart type="family">Ben Abacha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bethard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kirk</namePart>
<namePart type="family">Roberts</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Danielle</namePart>
<namePart type="family">Bitterman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we report our effort to tackle the challenge of extracting chemotimelines from EHR notes across a dataset of three cancer types. We focus on the two subtasks: 1) detection and classification of temporal relations given the annotated chemotherapy events and time expressions and 2) directly extracting patient chemotherapy timelines from EHR notes. We address both subtasks using Large Language Models. Our best-performing methods in both subtasks use Flan-T5, an instruction-tuned language model. Our proposed system achieves the highest average score in both subtasks. Our results underscore the effectiveness of finetuning general-domain large language models in domain-specific and unseen tasks.</abstract>
<identifier type="citekey">haddadan-etal-2024-lailab</identifier>
<identifier type="doi">10.18653/v1/2024.clinicalnlp-1.37</identifier>
<location>
<url>https://aclanthology.org/2024.clinicalnlp-1.37</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>382</start>
<end>393</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LAILab at Chemotimelines 2024: Finetuning sequence-to-sequence language models for temporal relation extraction towards cancer patient undergoing chemotherapy treatment
%A Haddadan, Shohreh
%A Le, Tuan-Dung
%A Duong, Thanh
%A Thieu, Thanh
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Bitterman, Danielle
%S Proceedings of the 6th Clinical Natural Language Processing Workshop
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F haddadan-etal-2024-lailab
%X In this paper, we report our effort to tackle the challenge of extracting chemotimelines from EHR notes across a dataset of three cancer types. We focus on the two subtasks: 1) detection and classification of temporal relations given the annotated chemotherapy events and time expressions and 2) directly extracting patient chemotherapy timelines from EHR notes. We address both subtasks using Large Language Models. Our best-performing methods in both subtasks use Flan-T5, an instruction-tuned language model. Our proposed system achieves the highest average score in both subtasks. Our results underscore the effectiveness of finetuning general-domain large language models in domain-specific and unseen tasks.
%R 10.18653/v1/2024.clinicalnlp-1.37
%U https://aclanthology.org/2024.clinicalnlp-1.37
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.37
%P 382-393
Markdown (Informal)
[LAILab at Chemotimelines 2024: Finetuning sequence-to-sequence language models for temporal relation extraction towards cancer patient undergoing chemotherapy treatment](https://aclanthology.org/2024.clinicalnlp-1.37) (Haddadan et al., ClinicalNLP-WS 2024)
ACL