Recurrent deep network models for clinical NLP tasks: Use case with sentence boundary disambiguation

Benjamin C. Knoll, Elizabeth A. Lindemann, Arian L. Albert, Genevieve B. Melton, Serguei V.S. Pakhomov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi-LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.com/NLPIE/clinical-sentences.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages198-202
Number of pages5
ISBN (Electronic)9781643680026
DOIs
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

    Fingerprint

Keywords

  • Machine Learning
  • Natural Language Processing
  • Neural Networks (Computer)

PubMed: MeSH publication types

  • Journal Article

Cite this

Knoll, B. C., Lindemann, E. A., Albert, A. L., Melton, G. B., & Pakhomov, S. V. S. (2019). Recurrent deep network models for clinical NLP tasks: Use case with sentence boundary disambiguation. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 198-202). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190211