Abstract
The online comment for doctors plays an important role in assisting patients to master the real medical situation and choose medical treatment, which greatly reduces the adverse effects of online medical market information asymmetry. but because ordinary patients lack professional medical knowledge and can not accurately and efficiently write comments that are useful to similar patients, we identify useful topics from online medical reviews and make combination recommendations based on patients with different diseases. guide patients to write comments from the most useful dimensions to alleviate the problem of information overload, thus maximizing the limited human resource utility. This study aimed at online medical platforms such as good doctors and micro-doctors collected online doctor reviews for major diseases and used LDA topic mining and fsQCA fuzzy set qualitative comparative analysis to analyze key topics that affect the usefulness of reviews and optimal topic combinations under different disease types.
| Original language | English |
|---|---|
| Article number | 012073 |
| Journal | Journal of Physics: Conference Series |
| Volume | 1757 |
| Online published | 3 Feb 2021 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 2020 International Conference on Computer Big Data and Artificial Intelligence, ICCBDAI 2020 - Changsha, China Duration: 24 Oct 2020 → 25 Oct 2020 |
Research Keywords
- fuzzy set analysis
- online medical review
- topic combination
- usefulness
Publisher's Copyright Statement
- This full text is made available under CC-BY 3.0. https://creativecommons.org/licenses/by/3.0/
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