Extracting Relations between Radiotherapy Treatment Details

Danielle Bitterman, Timothy Miller, David Harris, Chen Lin, Sean Finan, Jeremy Warner, Raymond Mak, Guergana Savova


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.
Anthology ID:
2020.clinicalnlp-1.21
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
194–200
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.21
DOI:
10.18653/v1/2020.clinicalnlp-1.21
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.21.pdf