Combating health misinformation with fusion-based credible retrieval techniques.
This study aims to combat health misinformation by enhancing the retrieval of credible health information using effective fusion-based techniques. It focuses on clustering-based subset selection to improve data fusion performance. Five clustering methods — two K-means variants, Agglomerative Hierarc...
| Publicado en: | Health Informatics Journal Vol. 31; no. 4; pp. 1 - 27 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Oct-Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190493591&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190493591 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Oct-Dec2025 vid: 31 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 190493591 190493591 190493591 10.1177/14604582251388860 190493591 ppf: 1 ppct: 26 formats: tig: atl: Combating health misinformation with fusion-based credible retrieval techniques. aug: au: Huang, Yidong Wu, Shengli Lu, Hu Geng, Xia Nugent, Chris affil: Department of Electronic Information and Computer Engineering, Engineering & Technical College of Chengdu University of Technology, Leshan, China sug: subj: Health Information Misinformation Prevention and Control Health Information Management Methods Information Retrieval Methods Algorithms Evaluation Human Data Management Cluster Analysis Methods Validity Reliability ab: This study aims to combat health misinformation by enhancing the retrieval of credible health information using effective fusion-based techniques. It focuses on clustering-based subset selection to improve data fusion performance. Five clustering methods — two K-means variants, Agglomerative Hierarchical (AH) clustering, BIRCH, and Chameleon — are evaluated for selecting optimal subsets of information retrieval systems. Experiments are conducted on two health-related datasets from the TREC challenge. The selected subsets are used in data fusion to boost retrieval quality and credibility. AH and BIRCH outperform other methods in identifying effective IR subsets. Using AH-based fusion of up to 20 systems results in a 60% gain in MAP and over a 30% increase in NDCG_UCC, a credibility-focused metric, compared to the best single system. Clustering-based fusion strategies significantly enhance the retrieval of trustworthy health content, helping to reduce misinformation. These findings support incorporating advanced data fusion into health information retrieval systems to improve access to reliable information. The source code of this research is publicly available at https://github.com/Gary752752/DataFusion. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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