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,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 287–295
- Language:
- URL:
- https://aclanthology.org/W17-2336
- DOI:
- 10.18653/v1/W17-2336
- Cite (ACL):
- Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, and David Suendermann-Oeft. 2017. Automated Preamble Detection in Dictated Medical Reports. In BioNLP 2017, pages 287–295, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Automated Preamble Detection in Dictated Medical Reports (Salloum et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W17-2336.pdf