Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes

Yoonkwon Yi, Zitao Shen, Anusha Bompelli, Fang Yu, Yanshan Wang, Rui Zhang

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

Abstract

Due to the absence of medications on Alzheimer's disease (AD), lifestyle exposures that could improve cognitive functionality have become extremely important. Thus, the objective of the study was to show the feasibility of using natural language processing (NLP) methods to extract lifestyle exposures from clinical texts. The proposed named-entity recognition (NER) task's results indicate that NLP models can detect lifestyle information (i.e., excessive diet, physical activity, sleep deprivation and substance abuse) from clinical notes, which has the potential for improving efficiency in information acquisition and accrual for AD clinical trials.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728153827
DOIs
StatePublished - Nov 2020
Event8th IEEE International Conference on Healthcare Informatics, ICHI 2020 - Virtual, Oldenburg, Germany
Duration: Nov 30 2020Dec 3 2020

Publication series

Name2020 IEEE International Conference on Healthcare Informatics, ICHI 2020

Conference

Conference8th IEEE International Conference on Healthcare Informatics, ICHI 2020
CountryGermany
CityVirtual, Oldenburg
Period11/30/2012/3/20

Keywords

  • Alzheimer's disease
  • Deep learning
  • Electronic health records
  • Information extraction
  • Lifestyle exposure
  • Machine learning
  • Natural language processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Decision Sciences (miscellaneous)
  • Modeling and Simulation
  • Medicine (miscellaneous)
  • Health Informatics
  • Health(social science)

Fingerprint Dive into the research topics of 'Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer's Disease from Clinical Notes'. Together they form a unique fingerprint.

Cite this