Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy. The ensemble showed increased performance over individual systems.
|Original language||English (US)|
|Title of host publication||MEDINFO 2019|
|Subtitle of host publication||Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics|
|Editors||Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi|
|Number of pages||2|
|State||Published - Aug 21 2019|
|Event||17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France|
Duration: Aug 25 2019 → Aug 30 2019
|Name||Studies in Health Technology and Informatics|
|Conference||17th World Congress on Medical and Health Informatics, MEDINFO 2019|
|Period||8/25/19 → 8/30/19|
Bibliographical noteFunding Information:
NIH NCATS UL1TR002494 and U01TR002062, NIGMS R01GM120079, and AHRQ R01HS022085 and R01HS024532.
© 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
- Emergency medical services
- Natural language processing