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dc.contributor.author | Sáez Silvestre, Carlos | es_ES |
dc.contributor.author | Robles Viejo, Montserrat | es_ES |
dc.contributor.author | García Gómez, Juan Miguel | es_ES |
dc.date.accessioned | 2015-05-27T11:39:39Z | |
dc.date.available | 2015-05-27T11:39:39Z | |
dc.date.issued | 2014-08-04 | |
dc.identifier.issn | 0962-2802 | |
dc.identifier.uri | http://hdl.handle.net/10251/50854 | |
dc.description.abstract | [EN] Biomedical data may be composed of individuals generated from distinct, meaningful sources. Due to possible contextual biases in the processes that generate data, there may exist an undesirable and unexpected variability among the probability distribution functions (PDFs) of the source subsamples, which, when uncontrolled, may lead to inaccurate or unreproducible research results. Classical statistical methods may have difficulties to undercover such variabilities when dealing with multi-modal, multi-type, multi-variate data. This work proposes two metrics for the analysis of stability among multiple data sources, robust to the aforementioned conditions, and defined in the context of data quality assessment. Specifically, a global probabilistic deviation (GPD) and a source probabilistic outlyingness (SPO) metrics are proposed. The first provides a bounded degree of the global multi-source variability, designed as an estimator equivalent to the notion of normalized standard deviation of PDFs. The second provides a bounded degree of the dissimilarity of each source to a latent central distribution. The metrics are based on the projection of a simplex geometrical structure constructed from the Jensen-Shannon distances among the sources PDFs. The metrics have been evaluated and demonstrated their correct behaviour on a simulated benchmark and with real multi-source biomedical data using the UCI Heart Disease dataset. The biomedical data quality assessment based on the proposed stability metrics may improve the efficiency and effectiveness of biomedical data exploitation and research. | es_ES |
dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by own IBIME funds under the UPV project Servicio de evaluacion y rating de la calidad de repositorios de datos biomedicos [UPV-2014-872] and the EU FP7 Project Help4Mood - A Computational Distributed System to Support the Treatment of Patients with Major Depression [ICT-248765]. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | SAGE Publications (UK and US) | es_ES |
dc.relation.ispartof | Statistical Methods in Medical Research | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Data variability | es_ES |
dc.subject | Data quality | es_ES |
dc.subject | Data reuse | es_ES |
dc.subject | Probability distribution distances | es_ES |
dc.subject | Information geometry | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.title | Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1177/0962280214545122 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/248765/EU/A Computational Distributed System to Support the Treatment of Patients with Major Depression/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//UPV-2014-872/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada | es_ES |
dc.description.bibliographicCitation | Sáez Silvestre, C.; Robles Viejo, M.; García Gómez, JM. (2014). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research. 1-25. https://doi.org/10.1177/0962280214545122 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1177/0962280214545122 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 25 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.senia | 268214 | |
dc.contributor.funder | European Commission | |
dc.contributor.funder | Universitat Politècnica de València |