{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T17:35:40Z","timestamp":1788024940826,"version":"build-2784847793"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["FKZ 01EE1409A"],"award-info":[{"award-number":["FKZ 01EE1409A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acamh.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Child Psychology Psychiatry"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co\u2010occurring disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      We used a well\u2010characterized clinical sample of individuals (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009481) and covered a range of additional overlapping diagnoses, including anxiety\u2010related disorders (ANX,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009122), ADHD (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009439), and conduct disorder (CD,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best\u2010estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co\u2010occurring ADHD, and an unspecific model using all available data. We employed nested cross\u2010validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We obtained very good overall sensitivity (0.89\u20130.94) and specificity (0.87\u20130.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML\u2010based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jcpp.13650","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T03:24:18Z","timestamp":1656645858000},"page":"16-26","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Using machine learning to improve diagnostic assessment of\n                    <scp>ASD<\/scp>\n                    in the light of specific differential and co\u2010occurring diagnoses"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7198-9923","authenticated-orcid":false,"given":"Martin","family":"Schulte\u2010R\u00fcther","sequence":"first","affiliation":[{"name":"Department of Child and Adolescent Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen  G\u00f6ttingen Germany"},{"name":"Leibniz ScienceCampus Primate Cognition  G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomas","family":"Kulvicius","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen  G\u00f6ttingen Germany"},{"name":"Department for Computational Neuroscience University of G\u00f6ttingen  G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0266-173X","authenticated-orcid":false,"given":"Sanna","family":"Stroth","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy University Hospital of Marburg, Philipps\u2010University Marburg  Marburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Wolff","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry TU Dresden  Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1873-7081","authenticated-orcid":false,"given":"Veit","family":"Roessner","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry TU Dresden  Dresden Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter B.","family":"Marschik","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen  G\u00f6ttingen Germany"},{"name":"Leibniz ScienceCampus Primate Cognition  G\u00f6ttingen Germany"},{"name":"Department of Women's and Children's Health, Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research Karolinska Institutet  Stockholm Sweden"},{"name":"iDN \u2010 interdisciplinary Developmental Neuroscience Division of Phoniatrics Medical University of Graz  Graz Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inge","family":"Kamp\u2010Becker","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy University Hospital of Marburg, Philipps\u2010University Marburg  Marburg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luise","family":"Poustka","sequence":"additional","affiliation":[{"name":"Department of Child and Adolescent Psychiatry and Psychotherapy University Medical Center G\u00f6ttingen  G\u00f6ttingen Germany"},{"name":"Leibniz ScienceCampus Primate Cognition  G\u00f6ttingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy039"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_2_7_4_1","unstructured":"AWMF. 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