@inproceedings{wang-etal-2024-wonder,
title = "Wonder at Chemotimelines 2024: {M}ed{T}imeline: An End-to-End {NLP} System for Timeline Extraction from Clinical Narratives",
author = "Wang, Liwei and
Lu, Qiuhao and
Li, Rui and
Fu, Sunyang and
Liu, Hongfang",
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-1.48",
doi = "10.18653/v1/2024.clinicalnlp-1.48",
pages = "483--487",
abstract = "Extracting timeline information from clinical narratives is critical for cancer research and practice using electronic health records (EHRs). In this study, we apply MedTimeline, our end-to-end hybrid NLP system combining large language model, deep learning with knowledge engineering, to the ChemoTimeLine challenge subtasks. Our experiment results in 0.83, 0.90, 0.84, and 0.53, 0.63, 0.39, respectively, for subtask1 and subtask2 in breast, melanoma and ovarian cancer.",
}
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<abstract>Extracting timeline information from clinical narratives is critical for cancer research and practice using electronic health records (EHRs). In this study, we apply MedTimeline, our end-to-end hybrid NLP system combining large language model, deep learning with knowledge engineering, to the ChemoTimeLine challenge subtasks. Our experiment results in 0.83, 0.90, 0.84, and 0.53, 0.63, 0.39, respectively, for subtask1 and subtask2 in breast, melanoma and ovarian cancer.</abstract>
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%0 Conference Proceedings
%T Wonder at Chemotimelines 2024: MedTimeline: An End-to-End NLP System for Timeline Extraction from Clinical Narratives
%A Wang, Liwei
%A Lu, Qiuhao
%A Li, Rui
%A Fu, Sunyang
%A Liu, Hongfang
%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 wang-etal-2024-wonder
%X Extracting timeline information from clinical narratives is critical for cancer research and practice using electronic health records (EHRs). In this study, we apply MedTimeline, our end-to-end hybrid NLP system combining large language model, deep learning with knowledge engineering, to the ChemoTimeLine challenge subtasks. Our experiment results in 0.83, 0.90, 0.84, and 0.53, 0.63, 0.39, respectively, for subtask1 and subtask2 in breast, melanoma and ovarian cancer.
%R 10.18653/v1/2024.clinicalnlp-1.48
%U https://aclanthology.org/2024.clinicalnlp-1.48
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.48
%P 483-487
Markdown (Informal)
[Wonder at Chemotimelines 2024: MedTimeline: An End-to-End NLP System for Timeline Extraction from Clinical Narratives](https://aclanthology.org/2024.clinicalnlp-1.48) (Wang et al., ClinicalNLP-WS 2024)
ACL