Integrating word embedding neural networks with pubmed abstracts to extract keyword proximity of chronic diseases

Ahmad P. Tafti, Yanshan Wang, Feichen Shen, Elham Sagheb, Paul Kingsbury, Hongfang Liu

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

1 Scopus citations

Abstract

Chronic diseases are a leading cause of morbidity and mortality worldwide. They are common enough to affect large numbers of patients, and the chronic nature makes them costly to both patients and healthcare providers. Diagnosis of many chronic diseases is challenged by variability in their clinical manifestations. Although chronic diseases bear a set of structured terminology aiming to standardize nomenclature of the presentation and outcomes of the disease, in practice there is a wide spectrum of terminology associated with these diseases across different venues such as clinical notes, biomedical literature, and health-related social media. Among these sources, the scientific articles published in the biomedical literature usually follow principled approaches to terminology and are thus especially valuable for extracting diseases keywords. Given the fact that it is very costly and time-consuming to manually extract disease terminology from a large column of scientific articles, we aim to utilize artificial neural network strategies to automatically extract vocabularies associated with a set of chronic diseases. Our finding indicates the feasibility of developing word embedding neural nets for autonomous keyword extraction and abstraction of chronic diseases.

Original languageEnglish (US)
Title of host publication2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108483
DOIs
StatePublished - May 2019
Event2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Chicago, United States
Duration: May 19 2019May 22 2019

Publication series

Name2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings

Conference

Conference2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
CountryUnited States
CityChicago
Period5/19/195/22/19

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Keywords

  • GloVe
  • Word embeddings
  • Word2vec

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Information Systems and Management
  • Biomedical Engineering
  • Health Informatics
  • Radiology Nuclear Medicine and imaging

Cite this

Tafti, A. P., Wang, Y., Shen, F., Sagheb, E., Kingsbury, P., & Liu, H. (2019). Integrating word embedding neural networks with pubmed abstracts to extract keyword proximity of chronic diseases. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings [8834626] (2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BHI.2019.8834626