@inproceedings{tan-etal-2024-kclab,
title = "{KCL}ab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction {--} Subtask2",
author = "Tan, Yukun and
Dede, Merve and
Chen, Ken",
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-1.40/",
doi = "10.18653/v1/2024.clinicalnlp-1.40",
pages = "417--421",
abstract = "This paper presents our participation in the Chemotimelines 2024 subtask2, focusing on the development of an end-to-end system for chemotherapy timeline extraction. We initially adopt a basic framework from subtask2, utilizing Apache cTAKES for entity recognition and a BERT-based model for classifying the temporal relationship between chemotherapy events and associated times. Subsequently, we enhance this pipeline through two key directions: first, by expanding the exploration of the system, achieved by extending the search dictionary of cTAKES with the UMLS database; second, by reducing false positives through preprocessing of clinical notes and implementing filters to reduce the potential errors from the BERT-based model. To validate the effectiveness of our framework, we conduct extensive experiments using clinical notes from breast, ovarian, and melanoma cancer cases. Our results demonstrate improvements over the previous approach."
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<abstract>This paper presents our participation in the Chemotimelines 2024 subtask2, focusing on the development of an end-to-end system for chemotherapy timeline extraction. We initially adopt a basic framework from subtask2, utilizing Apache cTAKES for entity recognition and a BERT-based model for classifying the temporal relationship between chemotherapy events and associated times. Subsequently, we enhance this pipeline through two key directions: first, by expanding the exploration of the system, achieved by extending the search dictionary of cTAKES with the UMLS database; second, by reducing false positives through preprocessing of clinical notes and implementing filters to reduce the potential errors from the BERT-based model. To validate the effectiveness of our framework, we conduct extensive experiments using clinical notes from breast, ovarian, and melanoma cancer cases. Our results demonstrate improvements over the previous approach.</abstract>
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%0 Conference Proceedings
%T KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction – Subtask2
%A Tan, Yukun
%A Dede, Merve
%A Chen, Ken
%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 tan-etal-2024-kclab
%X This paper presents our participation in the Chemotimelines 2024 subtask2, focusing on the development of an end-to-end system for chemotherapy timeline extraction. We initially adopt a basic framework from subtask2, utilizing Apache cTAKES for entity recognition and a BERT-based model for classifying the temporal relationship between chemotherapy events and associated times. Subsequently, we enhance this pipeline through two key directions: first, by expanding the exploration of the system, achieved by extending the search dictionary of cTAKES with the UMLS database; second, by reducing false positives through preprocessing of clinical notes and implementing filters to reduce the potential errors from the BERT-based model. To validate the effectiveness of our framework, we conduct extensive experiments using clinical notes from breast, ovarian, and melanoma cancer cases. Our results demonstrate improvements over the previous approach.
%R 10.18653/v1/2024.clinicalnlp-1.40
%U https://aclanthology.org/2024.clinicalnlp-1.40/
%U https://doi.org/10.18653/v1/2024.clinicalnlp-1.40
%P 417-421
Markdown (Informal)
[KCLab at Chemotimelines 2024: End-to-end system for chemotherapy timeline extraction – Subtask2](https://aclanthology.org/2024.clinicalnlp-1.40/) (Tan et al., ClinicalNLP 2024)
ACL