Disease Topic Modeling of Users' Inquiry Texts: A Text Mining-Based PQDR-LDA Model for Analyzing the Online Medical Records
Citation:
Xin Liu; Yanju Zhou; Zongrun Wang; Ajay Kumar; Baidyanath Biswas, Disease Topic Modeling of Users' Inquiry Texts: A Text Mining-Based PQDR-LDA Model for Analyzing the Online Medical Records, IEEE Transactions on Engineering Management, 2023Download Item:
Abstract:
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 allocation (LDA) and Twitter-LDA models to
propose an intelligent topic model, PQDR-LDA. Compared with
the Twitter-LDA model, the proposed model has a smaller per-
plexity value, stronger generalization ability, greater coherence
value, lower correlation between topics, and stronger ability in
extracting the disease information. It is found that the accuracy
of disease diagnosis is very low, and the user’s need for perceiving
the disease will be reduced while using the traditional model to mine
only the text of user questions on an online medical consultation
platform. The accuracy of disease diagnosis does not decrease while
only mining the doctor’s reply text. Disease information that is
more suitable for the consultation text can be obtained, which in
fact cannot meet the user’s real appeal for health, and reduces
the users’ needs in perceiving the disease. These findings have
important management implications for the platform’s operation
and decision-making. Besides, users will ask questions in more medical texts simultaneously, which makes things more complicated.
Unique management insights are obtained based on the disease
information mining of user consultation texts through multiple
consultation texts and multiple doctor replies.
Author's Homepage:
http://people.tcd.ie/biswasbDescription:
PUBLISHED
Author: Biswas, Baidyanath
Type of material:
Journal ArticleSeries/Report no:
IEEE Transactions on Engineering Management;Availability:
Full text availableSubject (TCD):
Information technology, e-commerceDOI:
https://doi.org/10.1109/TEM.2023.3307550ISSN:
0018-9391Metadata
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