Recognition of Hotspot Words for Disease Symptoms Incorporating Contextual Weight and Co-Occurrence Degree

Author:

Liu Qingxue1ORCID,Wang Lifang1,Chang Yuan2,Zhang Jixuan3

Affiliation:

1. School of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China

2. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China

3. Juxian No. 2 Middle School, Juxian 276500, China

Abstract

Identifying hotspot words associated with disease symptoms is paramount for disease prevention and diagnosis. In this study, we propose a novel method for hotspot word recognition in disease symptoms, integrating contextual weights and co-occurrence information. First, we establish the MDERank model, which incorporates contextual weights. This model identifies words that align well with comprehensive weights, forming a collection of disease symptom words. Next, we construct a graph network for disease symptom words within each time period. Utilizing the graph attention network model, we incorporate word co-occurrence degree to identify potential hotspot words associated with disease symptoms. We conducted experiments using user-generated posts from the Dingxiangyuan Forum as our data source. The results demonstrate that our proposed method significantly improves the extraction quality of disease symptom words compared to other existing methods. Furthermore, the performance of our constructed recognition model for disease symptom hotspot words surpasses that of alternative models.

Funder

Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities’ Association

Publisher

Hindawi Limited

Reference25 articles.

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