Natural Language Processing Methods for Detection of Influenza-Like Illness from Chief Complaints

Jia Hao Hsu, Ting Chia Weng, Chung Hsien Wu, Tzong Shiann Ho

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

Abstract

There are several existing studies on the application of medical chief complaints in disease classification. However, the lack of a standard vocabulary and high-quality interpretation of chief complaints hinder effective classification. This study uses a variety of methods to analyze chief complaints of preschool children to detect influenza-like illness. It is expected that a fast and effective tool can be designed to assist physicians in making diagnosis, and when facing a major outbreak, it can be quickly judged to control the outbreak as soon as possible. We use several natural language processing (NLP) technologies including deep learning methods, such as the currently popular BERT model, to classify Chinese chief complaints at emergency department to detect influenza-like illness. For model evaluation, the data in 2018 were used. The method based on BERT achieved the best accuracy of 72.87% for detection of influenza-like illness.

Original languageEnglish
Title of host publication2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1626-1630
Number of pages5
ISBN (Electronic)9789881476883
Publication statusPublished - 2020 Dec 7
Event2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
Duration: 2020 Dec 72020 Dec 10

Publication series

Name2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period20-12-0720-12-10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Signal Processing
  • Decision Sciences (miscellaneous)
  • Instrumentation

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