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dc.contributor.authorGrigull, Lorenz
dc.contributor.authorMehmecke, Sandra
dc.contributor.authorRother, Ann-Katrin
dc.contributor.authorBlöß, Susanne
dc.contributor.authorKlemann, Christian
dc.contributor.authorSchumacher, Ulrike
dc.contributor.authorMücke, Urs
dc.contributor.authorKortum, Xiaowei
dc.contributor.authorLechner, Werner
dc.contributor.authorKlawonn, Frank
dc.date.accessioned2019-10-29T13:40:40Z
dc.date.available2019-10-29T13:40:40Z
dc.date.issued2019-10-10
dc.identifier.citationPLoS One. 2019 Oct 10;14(10):e0222637. doi: 10.1371/journal.pone.0222637. eCollection 2019.en_US
dc.identifier.issn1932-6203
dc.identifier.pmid31600214
dc.identifier.doi10.1371/journal.pone.0222637
dc.identifier.urihttp://hdl.handle.net/10033/621996
dc.description.abstractBACKGROUND: Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established. OBJECTIVE: We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD. METHODS: 20 interviews with affected individuals with different RD, focusing on the time period before their diagnosis, were performed and qualitatively analyzed. Out of these pre-diagnostic experiences, we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) other common non-rare diseases (NRO) iii.) common chronic diseases (CD), iv.), or psychosomatic/somatoform disorders (PSY). Finally, four combined single machine learning methods and a fusion algorithm were used to distinguish the different answer patterns of the questionnaires. RESULTS: The questionnaire contained 53 questions. A total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY. CONCLUSION: Despite the great diversity in presentation and pathogenesis of each RD, patients with RD share surprisingly similar pre-diagnosis experiences. Our questionnaire and data-mining based approach successfully detected unique patterns in groups of individuals affected by a broad range of different rare diseases. Therefore, these results indicate distinct patterns that may be used for diagnostic support in RD.en_US
dc.language.isoenen_US
dc.publisherPublic Library of Science (PLoS)en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectGeneral Biochemistry, Genetics and Molecular Biologyen_US
dc.subjectGeneral Agricultural and Biological Sciencesen_US
dc.subjectGeneral Medicineen_US
dc.titleCommon pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?en_US
dc.typeArticleen_US
dc.contributor.departmentHZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.en_US
dc.identifier.journalPLOS ONEen_US
dc.source.volume14
dc.source.issue10
dc.source.beginpagee0222637
refterms.dateFOA2019-10-29T13:40:40Z


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