Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?
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...
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| Veröffentlicht in: | PloS one 2019-10, Vol.14 (10), p.e0222637-e0222637 |
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| creator | Grigull, Lorenz Mehmecke, Sandra Rother, Ann-Katrin Blöß, Susanne Klemann, Christian Schumacher, Ulrike Mücke, Urs Kortum, Xiaowei Lechner, Werner Klawonn, Frank |
| description | 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.
We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.
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.
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.
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. |
| doi_str_mv | 10.1371/journal.pone.0222637 |
| format | Article |
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We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.
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.
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.
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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0222637</identifier><identifier>PMID: 31600214</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adult ; Algorithms ; Amyotrophic lateral sclerosis ; Artificial Intelligence ; Biology and Life Sciences ; Care and treatment ; Chronic Disease - epidemiology ; Chronic diseases ; Computer and Information Sciences ; Computer science ; Data Mining ; Diagnosis ; Diagnostic software ; Diagnostic systems ; Female ; Health care access ; Health Personnel - statistics & numerical data ; Health Status ; Hematology ; Humans ; Learning algorithms ; Machine Learning ; Male ; Medical diagnosis ; Medical personnel ; Medical schools ; Medicine and Health Sciences ; Mining industry ; Oncology ; Pathogenesis ; Patients ; Pattern recognition ; Pattern recognition systems ; Pediatrics ; People and Places ; Physicians ; Practice ; Questionnaires ; Rare diseases ; Rare Diseases - classification ; Rare Diseases - diagnosis ; Rare Diseases - epidemiology ; Research and Analysis Methods ; Respiratory distress syndrome ; Social Sciences ; Somatoform disorders ; Surveys and Questionnaires ; Teaching methods ; Young Adult</subject><ispartof>PloS one, 2019-10, Vol.14 (10), p.e0222637-e0222637</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Grigull et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Grigull et al 2019 Grigull et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-23a6985bdd84b6d55eb688b897f75468e7c63d78a72b49d7d28abdbfec671d3b3</citedby><cites>FETCH-LOGICAL-c692t-23a6985bdd84b6d55eb688b897f75468e7c63d78a72b49d7d28abdbfec671d3b3</cites><orcidid>0000-0003-4255-5269</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786570/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786570/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31600214$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Pławiak, Paweł</contributor><creatorcontrib>Grigull, Lorenz</creatorcontrib><creatorcontrib>Mehmecke, Sandra</creatorcontrib><creatorcontrib>Rother, Ann-Katrin</creatorcontrib><creatorcontrib>Blöß, Susanne</creatorcontrib><creatorcontrib>Klemann, Christian</creatorcontrib><creatorcontrib>Schumacher, Ulrike</creatorcontrib><creatorcontrib>Mücke, Urs</creatorcontrib><creatorcontrib>Kortum, Xiaowei</creatorcontrib><creatorcontrib>Lechner, Werner</creatorcontrib><creatorcontrib>Klawonn, Frank</creatorcontrib><title>Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.
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.
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.
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.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Algorithms</subject><subject>Amyotrophic lateral sclerosis</subject><subject>Artificial Intelligence</subject><subject>Biology and Life Sciences</subject><subject>Care and treatment</subject><subject>Chronic Disease - epidemiology</subject><subject>Chronic diseases</subject><subject>Computer and Information Sciences</subject><subject>Computer science</subject><subject>Data Mining</subject><subject>Diagnosis</subject><subject>Diagnostic software</subject><subject>Diagnostic systems</subject><subject>Female</subject><subject>Health care access</subject><subject>Health Personnel - statistics & numerical data</subject><subject>Health Status</subject><subject>Hematology</subject><subject>Humans</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical personnel</subject><subject>Medical schools</subject><subject>Medicine and Health Sciences</subject><subject>Mining industry</subject><subject>Oncology</subject><subject>Pathogenesis</subject><subject>Patients</subject><subject>Pattern recognition</subject><subject>Pattern recognition systems</subject><subject>Pediatrics</subject><subject>People and Places</subject><subject>Physicians</subject><subject>Practice</subject><subject>Questionnaires</subject><subject>Rare diseases</subject><subject>Rare Diseases - classification</subject><subject>Rare Diseases - diagnosis</subject><subject>Rare Diseases - epidemiology</subject><subject>Research and Analysis Methods</subject><subject>Respiratory distress syndrome</subject><subject>Social Sciences</subject><subject>Somatoform disorders</subject><subject>Surveys and Questionnaires</subject><subject>Teaching methods</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu1DAQhiMEoqXwBggiISG42MWHxI5vQNWKw0qVKnG6tZx4vOsliVPbKZQn4XHxdtNqF_UC2ZLtyTf_eCaeLHuK0RxTjt9s3Oh71c4H18McEUIY5feyYywomTGC6P29_VH2KIQNQiWtGHuYHVHMECK4OM7-LFzXuT4fPMy0VavehWib3ICKo4eQ2z5NbS-tHlUb8p82rnNtjQEPfcy98pCOAVRIrIekErZ2lf-Aq9w4n-9phnEYnI87jcZ1wxjB29-g80HFtO2TQONWvY3W9e8eZw9MighPpvUk-_bh_dfFp9nZ-cfl4vRs1jBB4oxQxURV1lpXRc10WULNqqquBDe8LFgFvGFU80pxUhdCc00qVevaQMM41rSmJ9nzne7QuiCnogZJKKKCi6riiVjuCO3URg7edspfSaesvDY4v5LKpwRbkJhpClQYhIqi0KIRvBRa1UJpbohRKGm9naKNdQe6ScXyqj0QPfzS27VcuUvJeMVKvhV4NQl4dzFCiLKzoYG2VT248freJaJIYJzQF_-gd2c3USuVErC9cSlusxWVpwwRijETRaLmd1BpaOhsk16gscl-4PD6wCExEX7FlRpDkMsvn_-fPf9-yL7cY9eg2rgOrh23byYcgsUObLwLwYO5LTJGcttAN9WQ2waSUwMlt2f7P-jW6aZj6F84CBoY</recordid><startdate>20191010</startdate><enddate>20191010</enddate><creator>Grigull, Lorenz</creator><creator>Mehmecke, Sandra</creator><creator>Rother, Ann-Katrin</creator><creator>Blöß, Susanne</creator><creator>Klemann, Christian</creator><creator>Schumacher, Ulrike</creator><creator>Mücke, Urs</creator><creator>Kortum, Xiaowei</creator><creator>Lechner, Werner</creator><creator>Klawonn, Frank</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4255-5269</orcidid></search><sort><creationdate>20191010</creationdate><title>Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?</title><author>Grigull, Lorenz ; Mehmecke, Sandra ; Rother, Ann-Katrin ; Blöß, Susanne ; Klemann, Christian ; Schumacher, Ulrike ; Mücke, Urs ; Kortum, Xiaowei ; Lechner, Werner ; Klawonn, Frank</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-23a6985bdd84b6d55eb688b897f75468e7c63d78a72b49d7d28abdbfec671d3b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Algorithms</topic><topic>Amyotrophic lateral sclerosis</topic><topic>Artificial Intelligence</topic><topic>Biology and Life Sciences</topic><topic>Care and treatment</topic><topic>Chronic Disease - epidemiology</topic><topic>Chronic diseases</topic><topic>Computer and Information Sciences</topic><topic>Computer science</topic><topic>Data Mining</topic><topic>Diagnosis</topic><topic>Diagnostic software</topic><topic>Diagnostic systems</topic><topic>Female</topic><topic>Health care access</topic><topic>Health Personnel - statistics & numerical data</topic><topic>Health Status</topic><topic>Hematology</topic><topic>Humans</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical personnel</topic><topic>Medical schools</topic><topic>Medicine and Health Sciences</topic><topic>Mining industry</topic><topic>Oncology</topic><topic>Pathogenesis</topic><topic>Patients</topic><topic>Pattern recognition</topic><topic>Pattern recognition systems</topic><topic>Pediatrics</topic><topic>People and Places</topic><topic>Physicians</topic><topic>Practice</topic><topic>Questionnaires</topic><topic>Rare diseases</topic><topic>Rare Diseases - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Grigull, Lorenz</au><au>Mehmecke, Sandra</au><au>Rother, Ann-Katrin</au><au>Blöß, Susanne</au><au>Klemann, Christian</au><au>Schumacher, Ulrike</au><au>Mücke, Urs</au><au>Kortum, Xiaowei</au><au>Lechner, Werner</au><au>Klawonn, Frank</au><au>Pławiak, Paweł</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-10-10</date><risdate>2019</risdate><volume>14</volume><issue>10</issue><spage>e0222637</spage><epage>e0222637</epage><pages>e0222637-e0222637</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.
We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.
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.
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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31600214</pmid><doi>10.1371/journal.pone.0222637</doi><tpages>e0222637</tpages><orcidid>https://orcid.org/0000-0003-4255-5269</orcidid><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_2303979887 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adolescent Adult Algorithms Amyotrophic lateral sclerosis Artificial Intelligence Biology and Life Sciences Care and treatment Chronic Disease - epidemiology Chronic diseases Computer and Information Sciences Computer science Data Mining Diagnosis Diagnostic software Diagnostic systems Female Health care access Health Personnel - statistics & numerical data Health Status Hematology Humans Learning algorithms Machine Learning Male Medical diagnosis Medical personnel Medical schools Medicine and Health Sciences Mining industry Oncology Pathogenesis Patients Pattern recognition Pattern recognition systems Pediatrics People and Places Physicians Practice Questionnaires Rare diseases Rare Diseases - classification Rare Diseases - diagnosis Rare Diseases - epidemiology Research and Analysis Methods Respiratory distress syndrome Social Sciences Somatoform disorders Surveys and Questionnaires Teaching methods Young Adult |
| title | Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition? |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T10%3A59%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Common%20pre-diagnostic%20features%20in%20individuals%20with%20different%20rare%20diseases%20represent%20a%20key%20for%20diagnostic%20support%20with%20computerized%20pattern%20recognition?&rft.jtitle=PloS%20one&rft.au=Grigull,%20Lorenz&rft.date=2019-10-10&rft.volume=14&rft.issue=10&rft.spage=e0222637&rft.epage=e0222637&rft.pages=e0222637-e0222637&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0222637&rft_dat=%3Cgale_plos_%3EA602311694%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2303979887&rft_id=info:pmid/31600214&rft_galeid=A602311694&rft_doaj_id=oai_doaj_org_article_16d3e39f00444d9c9759dab9ad7f2fa0&rfr_iscdi=true |