Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources

Mayer, Miguel Ángel; Karampiperis, Pythagoras; Kukurikos, Antonis; Karkaletsis, Vangelis; Stamatakis, Kostas; Villarroel, Dagmar; Leis, Ángela

doi:10.1177/1460458211405004

Cited by 11 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mayer et al, for example, use an RDF schema to assist in labelling the quality standards of medical websites 27 . Another paper by Mayer et al describes a platform to automatically generate metadata descriptions that can be used to label the trustworthiness of the content of medical websites 28 . This metadata can be accessed through standard search engines, and the fact that the data are machine readable allows for more targeted querying, as well as potentially advancing interoperability.…”

Section: Discussionmentioning

confidence: 99%

Data linkage in medical science using the resource description framework: the AVERT model

et al. 2019

View full text Add to dashboard Cite

There is an ongoing challenge as to how best manage and understand ‘big data’ in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This “AVERT model” provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.

show abstract

Section: Discussionmentioning

confidence: 99%

Data linkage in medical science using the resource description framework: the AVERT model

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Another difficulty is choosing among dozens of guidelines [23,24]. These guidelines have been combined with automatic approaches that extract certain quality indicators [11,12,26-28] used for online health information retrieval. However, automatic approaches are still not widely used.…”

Section: Introductionmentioning

confidence: 99%

HealthTrust: A Social Network Approach for Retrieving Online Health Videos

Fernández-Luque¹,

Karlsen²,

Melton³

2012

J Med Internet Res

View full text Add to dashboard Cite

BackgroundSocial media are becoming mainstream in the health domain. Despite the large volume of accurate and trustworthy health information available on social media platforms, finding good-quality health information can be difficult. Misleading health information can often be popular (eg, antivaccination videos) and therefore highly rated by general search engines. We believe that community wisdom about the quality of health information can be harnessed to help create tools for retrieving good-quality social media content.ObjectivesTo explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content.MethodsWe designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels.ResultsHealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust’s filtering allowed only 3 bad channels (15%) versus 8 (40%) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r 10 = .65, P = .02) and a trend toward significance with health consumers (r 7 = .65, P = .06) with videos on hemoglobinA1 c, but it did not perform as well with diabetic foot videos.ConclusionsThe trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities.

show abstract

“…As a result, the AQUA system (Mayer et al, 2011) was designed to support the main tasks of the web content accreditation process, that is: identification of unlabeled resources having health-related content; visit and review of the identified resources; generation of content labels for the reviewed resources, and monitoring the labeled resources.…”

Section: Aqua System Overviewmentioning

confidence: 99%

The POWDER protocol as infrastructure to serving and compressing semantic data

Konstantopoulos

Archer

Karampiperis

et al. 2012

IJMSO

View full text Add to dashboard Cite

Abstract:The POWDER protocol is a Semantic Web technology that takes advantage of natural groupings of URIs in order to annotate all the resources in a regular expressiondelineated sub-space of the URI space. POWDER is a mechanism for accreditation, trustmarking, and resource discovery, emphasising the publishing of attributed metadata by third parties and trusted authorities. Demonstrating its versatility, it has also been deployed in unforeseen use cases, such as repository compression. In this paper we present the POWDER protocol, explain its position in the Semantic Web architecture, expose and discuss current implementations and use cases, and future directions.

show abstract

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources

Cited by 11 publications

References 13 publications

Data linkage in medical science using the resource description framework: the AVERT model

Data linkage in medical science using the resource description framework: the AVERT model

HealthTrust: A Social Network Approach for Retrieving Online Health Videos

The POWDER protocol as infrastructure to serving and compressing semantic data

Contact Info

Product

Resources

About