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Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

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Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

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dc.contributor.author Sáez Silvestre, Carlos es_ES
dc.contributor.author Pereira Rodrigues, Pedro es_ES
dc.contributor.author Gama, João es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.contributor.author García Gómez, Juan Miguel es_ES
dc.date.accessioned 2015-05-26T11:16:12Z
dc.date.available 2015-05-26T11:16:12Z
dc.date.issued 2014-09-02
dc.identifier.issn 1384-5810
dc.identifier.uri http://hdl.handle.net/10251/50768
dc.description The final publication is available at Springer via http://dx.doi.org/DOI 10.1007/s10618-014-0378-6. Published online. es_ES
dc.description.abstract Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset. es_ES
dc.description.sponsorship The work by C Saez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. The authors thank Dr. Gregor Stiglic, from the Univeristy of Maribor, Slovenia, for his support on the NHDS data. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Data Mining and Knowledge Discovery es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Data quality es_ES
dc.subject Change detection es_ES
dc.subject Information theory es_ES
dc.subject Information geometry es_ES
dc.subject Visual analytics es_ES
dc.subject Biomedical data es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10618-014-0378-6
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.; Pereira Rodrigues, P.; Gama, J.; Robles Viejo, M.; García Gómez, JM. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery. 28:1-1. doi:10.1007/s10618-014-0378-6 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://link.springer.com/article/10.1007/s10618-014-0378-6 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 1 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 28 es_ES
dc.relation.senia 269167
dc.contributor.funder European Commission es_ES
dc.contributor.funder Erasmus+ es_ES
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