Quality labelling of medical web content

Mayer, Miguel Ángel; Karkaletsis, Vangelis; Archer, Phil; Ruiz, Pilar de Fuentes; Stamatakis, Konstantinos; Leis, Ángela

doi:10.1177/1460458206061230

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…Angel et al work on a system that measures the quality of medical websites [17,23]. Eysenbach analyzed health-related searches on the World Wide Web [9] and reported that in Google 6.75 Million health-related searches per day are conducted.…”

Section: Analysis and Assessing Social Media Data In Medicinementioning

confidence: 99%

How valuable is medical social media data? Content analysis of the medical web

Denecke

Nejdl

2009

Information Sciences

165

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Section: Analysis and Assessing Social Media Data In Medicinementioning

confidence: 99%

How valuable is medical social media data? Content analysis of the medical web

Denecke

Nejdl

2009

Information Sciences

165

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“…Several prior publications have addressed the use of RDF approaches to improve biomedical data annotation. 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 .…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…It is known that the quality of these websites is very difficult to assess and evaluate (Mayer et al 2005 …”

Section: Data Acquisition (Health On Net Foundation)mentioning

confidence: 99%

A Novel Semi-Automated Ontology Construction Framework (SOCF) for Psoriasis Detection: Pioneering the Psoriasis Risk Assessment Remedy (PRAR) Database

Selvam¹,

Shomona²

2016

SIC

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Ontology provides an organizational framework of concepts and a system that depicts hierarchical and associative relationships pertaining to an application domain. The possibility of reuse and data sharing permitted by ontology, along with the formal structure coupled with hierarchies of concepts and their inter-relationships offer the opportunity to draw complex inferences and reasoning. This rationale was the motivation to construct an ontology for Psoriasis Risk Assessment and Remedy (PRAR). This paper targets two issues: (i) Need for a medical database to derive Ontology (ii) Methodology for design of Semi-Automated Ontology Construction framework (SOCF) from pioneered data. Psoriasis is one of the most recurrent skin issues in India and the world at large and hence this paper targeted the need to generate a Psoriasis Remedy Database and automatically infer the relations between the Symptoms, Causes and Treatment through Semi-Automated Ontology Reasoning and Inference. The proposed system incorporated two phases: Formulation of a novel database for Psoriasis Risk Assessment Remedy (PRAR) (ii) Articulation of a novel framework for Psoriasis detection through computational modeling and Ontology Construction. The proposed methodology was tested on 112 samples from the authenticated UCI Machine Learning Repository. The ontology developed using the proposed SOCF mapped the risk factors and remedies for Psoriasis detection with 98.7% accuracy, this being reported for the first time.

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Quality labelling of medical web content

Cited by 17 publications

References 7 publications

How valuable is medical social media data? Content analysis of the medical web

How valuable is medical social media data? Content analysis of the medical web

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

A Novel Semi-Automated Ontology Construction Framework (SOCF) for Psoriasis Detection: Pioneering the Psoriasis Risk Assessment Remedy (PRAR) Database

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