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

4区 计算机科学 Q3 Computer Science
Qingxue Liu, Lifang Wang, Yuan Chang, Jixuan Zhang
{"title":"Recognition of Hotspot Words for Disease Symptoms Incorporating Contextual Weight and Co-Occurrence Degree","authors":"Qingxue Liu, Lifang Wang, Yuan Chang, Jixuan Zhang","doi":"10.1155/2024/7863381","DOIUrl":null,"url":null,"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.","PeriodicalId":22091,"journal":{"name":"Scientific Programming","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Programming","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2024/7863381","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

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.
结合上下文权重和共现程度识别疾病症状热点词汇
识别与疾病症状相关的热点词汇对于疾病的预防和诊断至关重要。在本研究中,我们提出了一种整合上下文权重和共现信息的疾病症状热点词识别新方法。首先,我们建立了包含上下文权重的 MDERank 模型。该模型可识别出与综合权重吻合度较高的词语,从而形成疾病症状词语集合。接下来,我们为每个时间段内的疾病症状词构建一个图网络。利用图注意力网络模型,我们结合词语共现程度来识别与疾病症状相关的潜在热点词语。我们使用定襄园论坛的用户生成帖子作为数据源进行了实验。结果表明,与其他现有方法相比,我们提出的方法显著提高了疾病症状词的提取质量。此外,我们构建的疾病症状热点词识别模型的性能也超过了其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Scientific Programming
Scientific Programming 工程技术-计算机:软件工程
自引率
0.00%
发文量
1059
审稿时长
>12 weeks
期刊介绍: Scientific Programming is a peer-reviewed, open access journal that provides a meeting ground for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing. The journal publishes papers on language, compiler, and programming environment issues for scientific computing. Of particular interest are contributions to programming and software engineering for grid computing, high performance computing, processing very large data sets, supercomputing, visualization, and parallel computing. All languages used in scientific programming as well as scientific programming libraries are within the scope of the journal.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信