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Predicting Related Medical Topics from Social Media |
Wu Shengnan1,Tian Ruonan2,Pu Hongjun1,Liang Wenqi2,Zhang Yafei2,Yu Qi1,He Peifeng1,2() |
1School of Management, Shanxi Medical University, Taiyuan 030000, China 2School of Humanities and Social Sciences, Shanxi Medical University, Taiyuan 030000, China |
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Abstract [Objective] This paper proposes a new knowledge discovery method for social media, aiming to predict the topic-related opportunities and emerging topics in medicine.[Methods] We developed a method combining the Co-LDA topic model and the link prediction algorithm to identify topic association opportunities. We examined the new model with data on diabetes drugs from social media. [Results] The AUC value of the prediction for the common network link without the right topics was higher than those with the right topics, while the Katz index is the optimal one. The future research on diabetes drugs is most likely to be related to the improvement of pharmacodynamic research and treatment plans. The development of the pharmaceutical industry and the new drug indications were related. [Limitations] We did not conduct multi-level analysis with emotional and time dimensions, and the new algorithm is very complex and did not perform well with poor network connectivity. [Conclusions] The proposed method could effectively predict the topic association opportunities in the field of medicine.
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Received: 15 June 2021
Published: 20 January 2022
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Fund:National Natural Science Foundation of China(71804102);National Natural Science Foundation of China(71573162);Philosophy and Social Science Research Project of Colleges and Universities in Shanxi Province(71573162) |
Corresponding Authors:
He Peifeng,ORCID:0000-0002-3742-6983
E-mail: hepeifeng2006@126.com
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