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Disease Topic Modeling of Users' Inquiry Texts: A Text Mining-Based PQDR-LDA Model for Analyzing the Online Medical Records

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成果类型:
期刊论文
作者:
Liu, Xin;Zhou, Yanju;Wang, Zongrun;Kumar, Ajay;Biswas, Baidyanath
通讯作者:
Wang, ZR
作者机构:
[Liu, Xin] Hengyang Normal Univ, Coll Econ & Management, Hengyang 421007, Peoples R China.
[Wang, Zongrun; Zhou, Yanju] Cent South Univ, Sch Business, Changsha 410017, Peoples R China.
[Kumar, Ajay] EMLYON Business Sch, F-69130 Ecully, France.
[Biswas, Baidyanath] Trinity Coll Dublin, Trinity Business Sch, Dublin D02 F6N2, Ireland.
通讯机构:
[Wang, ZR ] C
Cent South Univ, Sch Business, Changsha 410017, Peoples R China.
语种:
英文
关键词:
Analytical models;Big data analytics;Cognition;Costs;Data mining;data science in healthcare;Diseases;healthcare technology;Medical diagnostic imaging;Medical services;online medicine;PQDR-LDA model;text mining
期刊:
IEEE Transactions on Engineering Management
ISSN:
0018-9391
年:
2023
页码:
1-19
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 72091515, 71871232 and 91846301)
机构署名:
本校为第一机构
院系归属:
经济与管理学院
摘要:
Disease information mining is one of the critical factors affecting users' perception of the disease and has attracted extensive attention from the information management community in recent years. If the mined disease information is incompatible with the disease information perceived by the user, it will eventually lead to the loss of users from the online medical consultation platform, degrading its operation and management. Using existing models to mine disease information leads to significant errors when users perceive the disease. Therefore, this research extends the latent Dirichlet allo...

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