Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5306
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dc.contributor.authorSaez,Cen
dc.contributor.authorRodrigues,Pen
dc.contributor.authorJoão Gamaen
dc.contributor.authorRobles,Men
dc.contributor.authorGarcia Gomez,JMen
dc.date.accessioned2018-01-03T10:34:35Z-
dc.date.available2018-01-03T10:34:35Z-
dc.date.issued2015en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/5306-
dc.identifier.urihttp://dx.doi.org/10.1007/s10618-014-0378-6en
dc.description.abstractKnowledge 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.en
dc.languageengen
dc.relation5120en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleProbabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data qualityen
dc.typearticleen
dc.typePublicationen
Appears in Collections:LIAAD - Articles in International Journals

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