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dc.contributor.authorAlves, Rui
dc.contributor.authorPiñol, Marc
dc.contributor.authorVilaplana Mayoral, Jordi
dc.contributor.authorTeixidó Torrelles, Ivan
dc.contributor.authorCruz, Joaquim
dc.contributor.authorComas, Jorge
dc.contributor.authorVilaprinyo Terré, Ester
dc.contributor.authorSorribas Tello, Albert
dc.contributor.authorSolsona Tehàs, Francesc
dc.date.accessioned2016-09-13T11:42:34Z
dc.date.available2016-09-13T11:42:34Z
dc.date.issued2016
dc.identifier.issn2167-8359
dc.identifier.urihttp://hdl.handle.net/10459.1/57795
dc.description.abstractIntroduction. Most documented rare diseases have genetic origin. Because of their low individual frequency, an initial diagnosis based on phenotypic symptoms is not always easy, as practitioners might never have been exposed to patients suffering from the relevant disease. It is thus important to develop tools that facilitate symptom-based initial diagnosis of rare diseases by clinicians. In this work we aimed at developing a computational approach to aid in that initial diagnosis. We also aimed at implementing this approach in a user friendly web prototype. We call this tool Rare Disease Discovery. Finally, we also aimed at testing the performance of the prototype. Methods. Rare Disease Discovery uses the publicly available ORPHANET data set of association between rare diseases and their symptoms to automatically predict the most likely rare diseases based on a patient’s symptoms. We apply the method to retrospectively diagnose a cohort of 187 rare disease patients with confirmed diagnosis. Subsequently we test the precision, sensitivity, and global performance of the system under different scenarios by running large scale Monte Carlo simulations. All settings account for situations where absent and/or unrelated symptoms are considered in the diagnosis. Results. We find that this expert system has high diagnostic precision (≥80%) and sensitivity (≥99%), and is robust to both absent and unrelated symptoms. Discussion. The Rare Disease Discovery prediction engine appears to provide a fast and robust method for initial assisted differential diagnosis of rare diseases. We coupled this engine with a user-friendly web interface and it can be freely accessed at http://disease-discovery.udl.cat/. The code and most current database for the whole project can be downloaded from https://github.com/Wrrzag/DiseaseDiscovery/tree/no_classifiers.ca_ES
dc.description.sponsorshipThis work was partially supported by the MEyC under contracts TIN2014-53234-C2- 2-R, TIN2011-28689-C02-02 and BFU2010-17704 and by Universitat de Lleida and Departament de Ciències Mèdiques Bàsiques with bridge grants to RA. The authors are members of the research groups 2014-SGR163 and 2014-SGR243, funded by the Generalitat de Catalunya.ca_ES
dc.language.isoengca_ES
dc.publisherPeerJca_ES
dc.relationMINECO/PN2013-2016/TIN2014-53234-C2-2-Rca_ES
dc.relationMICINN/PN2008-2011/TIN2011-28689-C02-02ca_ES
dc.relationMICINN/PN2008-2011/BFU2010-17704ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.7717/peerj.2211ca_ES
dc.relation.ispartofPeerJ, 2016, núm. 4, e2211ca_ES
dc.rightscc-by, (c) Alves et al., 2016ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectComputer assisted diagnosisca_ES
dc.subjectRare diseasesca_ES
dc.subjecteHealthca_ES
dc.subjectFamily doctorsca_ES
dc.subjectUser-friendly webserverca_ES
dc.titleComputer-assisted initial diagnosis of rare diseasesca_ES
dc.typearticleca_ES
dc.identifier.idgrec024564
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.7717/peerj.2211


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