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dc.contributor.authorZamora, Martí
dc.contributor.authorBaradad, Manel
dc.contributor.authorAmado, Ester
dc.contributor.authorCordomí, Sílvia
dc.contributor.authorLimón, Esther
dc.contributor.authorRibera, Juliana
dc.contributor.authorArias Vicente, Marta
dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-02-10T13:17:39Z
dc.date.available2016-02-10T13:17:39Z
dc.date.issued2015
dc.identifier.citationZamora, M., Baradad, M., Amado, E., Cordomí, S., Limón, E., Ribera, J., Arias, M., Gavaldà, R. Characterizing chronic disease and polymedication prescription patterns from electronic health records. A: IEEE International Conference on Data Science and Advanced Analytics. "Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics". Paris: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1-9.
dc.identifier.isbn978-1-4673-8273-1
dc.identifier.urihttp://hdl.handle.net/2117/82778
dc.description.abstractPopulation aging in developed countries brings an increased prevalence of chronic disease and of polymedication-patients with several prescribed types of medication. Attention to chronic, polymedicated patients is a priority for its high cost and the associated risks, and tools for analyzing, understanding, and managing this reality are becoming necessary. We describe a prototype of a system for discovering, analyzing, and visualizing the co-occurrence of diagnostics, interventions, and medication prescriptions in a large patient database. The final tool is intended to be used both by health managers and planners and for primary care clinicians in direct contact with patients (for example for detecting unusual disease patterns and incorrect or missing medication). At the core of the analysis module there is a representation of diagnostics and medications as a hypergraph, and the most crucial functionalities rely on hypergraph transversal/variants of association rule discovery methods, with particular emphasis on discovering surprising or alarming combinations. The test database comes from the primary care system in the area of Barcelona for 2013, with over 1.6 million potential patients and almost 20 million diagnostics and prescriptions.
dc.format.extent9 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject.lcshDiagnosis -- Data processing
dc.subject.lcshData mining
dc.subject.otherDiseases
dc.subject.otherElectronic health records
dc.subject.otherGraph theory
dc.subject.otherPatient diagnosis
dc.titleCharacterizing chronic disease and polymedication prescription patterns from electronic health records
dc.typeConference report
dc.subject.lemacDiagnòstic -- Informàtica
dc.subject.lemacMineria de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.identifier.doi10.1109/DSAA.2015.7344870
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7344870
dc.rights.accessOpen Access
local.identifier.drac17501300
dc.description.versionPostprint (author's final draft)
local.citation.authorZamora, M.; Baradad, M.; Amado, E.; Cordomí, S.; Limón, E.; Ribera, J.; Arias, M.; Gavaldà, R.
local.citation.contributorIEEE International Conference on Data Science and Advanced Analytics
local.citation.pubplaceParis
local.citation.publicationNameProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics
local.citation.startingPage1
local.citation.endingPage9


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