@inproceedings{bitterman-etal-2020-extracting,
title = "Extracting Relations between Radiotherapy Treatment Details",
author = "Bitterman, Danielle and
Miller, Timothy and
Harris, David and
Lin, Chen and
Finan, Sean and
Warner, Jeremy and
Mak, Raymond and
Savova, Guergana",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.clinicalnlp-1.21",
doi = "10.18653/v1/2020.clinicalnlp-1.21",
pages = "194--200",
abstract = "We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.",
}
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<abstract>We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.</abstract>
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%0 Conference Proceedings
%T Extracting Relations between Radiotherapy Treatment Details
%A Bitterman, Danielle
%A Miller, Timothy
%A Harris, David
%A Lin, Chen
%A Finan, Sean
%A Warner, Jeremy
%A Mak, Raymond
%A Savova, Guergana
%S Proceedings of the 3rd Clinical Natural Language Processing Workshop
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F bitterman-etal-2020-extracting
%X We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.
%R 10.18653/v1/2020.clinicalnlp-1.21
%U https://aclanthology.org/2020.clinicalnlp-1.21
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.21
%P 194-200
Markdown (Informal)
[Extracting Relations between Radiotherapy Treatment Details](https://aclanthology.org/2020.clinicalnlp-1.21) (Bitterman et al., ClinicalNLP 2020)
ACL
- Danielle Bitterman, Timothy Miller, David Harris, Chen Lin, Sean Finan, Jeremy Warner, Raymond Mak, and Guergana Savova. 2020. Extracting Relations between Radiotherapy Treatment Details. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 194–200, Online. Association for Computational Linguistics.