A Thematic Portfolio and Recommended Study on the Usefulness of Online Medical Reviews Based on QCA Methods

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yonghong Wang
  • Lei Huang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number012073
Journal / PublicationJournal of Physics: Conference Series
Volume1757
Online published3 Feb 2021
Publication statusPublished - 2021

Conference

Title2020 International Conference on Computer Big Data and Artificial Intelligence, ICCBDAI 2020
PlaceChina
CityChangsha
Period24 - 25 October 2020

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.

Research Area(s)

  • fuzzy set analysis, online medical review, topic combination, usefulness