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

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1626-1630
頁數5
ISBN(電子)9789881476883
出版狀態Published - 2020 12月 7
事件2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
持續時間: 2020 12月 72020 12月 10

出版系列

名字2020 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
國家/地區New Zealand
城市Virtual, Auckland
期間20-12-0720-12-10

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 電腦視覺和模式識別
  • 硬體和架構
  • 訊號處理
  • 決策科學(雜項)
  • 儀器

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