@inproceedings{salek-faramarzi-etal-2023-context,
title = "Context-aware Medication Event Extraction from Unstructured Text",
author = "Salek Faramarzi, Noushin and
Patel, Meet and
Bandarupally, Sai Harika and
Banerjee, Ritwik",
editor = "Naumann, Tristan and
Ben Abacha, Asma and
Bethard, Steven and
Roberts, Kirk and
Rumshisky, Anna",
booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.clinicalnlp-1.11",
doi = "10.18653/v1/2023.clinicalnlp-1.11",
pages = "86--95",
abstract = "Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.",
}
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<abstract>Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.</abstract>
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%0 Conference Proceedings
%T Context-aware Medication Event Extraction from Unstructured Text
%A Salek Faramarzi, Noushin
%A Patel, Meet
%A Bandarupally, Sai Harika
%A Banerjee, Ritwik
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F salek-faramarzi-etal-2023-context
%X Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.
%R 10.18653/v1/2023.clinicalnlp-1.11
%U https://aclanthology.org/2023.clinicalnlp-1.11
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.11
%P 86-95
Markdown (Informal)
[Context-aware Medication Event Extraction from Unstructured Text](https://aclanthology.org/2023.clinicalnlp-1.11) (Salek Faramarzi et al., ClinicalNLP 2023)
ACL