| Paper ID | C-2-3.6 |
| Paper Title |
Natural Language Processing Methods for Detection of Influenza-Like Illness from Chief Complaints |
| Authors |
Jia-Hao Hsu, Ting-Chia Weng, Chung-Hsien Wu, Tzong-Shiann Ho, National Cheng Kung University, Taiwan |
| Session |
C-2-3: Machine Learning and Data Analysis 1 |
| Time | Wednesday, 09 December, 17:15 - 19:15 |
| Presentation Time: | Wednesday, 09 December, 18:30 - 18:45 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Machine Learning and Data Analytics (MLDA): |
| 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. |