Automated Preamble Detection in Dictated Medical Reports

Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft


Abstract
Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.
Anthology ID:
W17-2336
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
287–295
Language:
URL:
https://www.aclweb.org/anthology/W17-2336
DOI:
10.18653/v1/W17-2336
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2336.pdf