Online Forum Authenticity: Big Data Analytics in Healthcare

被引：0
|
作者：
Zhan, Ge [1 ]
机构：
[1] Shenzhen Pianpian Technol Ltd, HKUST Innovat & Entrepreneurship Ctr, Shenzhen, Peoples R China
来源：
ICMLC 2019: 2019 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING | 2019年
关键词：
Doctor quality; doctor-patient interaction; authenticity; patient review; PATIENT;
D O I：
10.1145/3318299.3318395
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is difficult to discern the authenticity online reviews, which is critical particularly in a setting of patient-doctor online forum. In this paper, a model on the detection of doctor quality has been developed and tested with online big data. In this study, a database with 31,646 online reviews was compiled. Text mining and word cloud analysis results indicate that the model provides an effective solution to assess the quality of doctors registered in online forum, the quality of doctor-patient online interaction, and patients' overall perception. A guideline has been provided to evaluate doctor authenticity.
引用
收藏
页码：290 / 294
页数：5
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