{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:24:58Z","timestamp":1787250298857,"version":"build-2736575974"},"reference-count":112,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Hikikomori, or prolonged social withdrawal, is an issue of global relevance. The HRI-15 is a brief tool for its assessment. This study enhances its utility by adding clinical thresholds to identify risk and person-centered clinical profiles, and by developing a machine-learning(ML)\u2013based computerized adaptive test (CAT) to enable rapid screening. Data from a national survey of Italian adolescents (\n                    <jats:italic>N<\/jats:italic>\n                    = 8,755) were used to conduct ROC analysis and latent profile analysis (LPA). The findings indicated that a score of \u2265 42 achieved optimal classification performance, and four profiles with distinct meanings and systematic differences in anxiety, depression, impulsivity, and risk behaviors were identified. A multivariate conditional inference tree was estimated to develop an ML-based adaptive version of the instrument. The CAT reduced administered items by 53% (7.03\/15), accurately reproducing full-length scores (\n                    <jats:italic>r<\/jats:italic>\n                    =\u2002.77\u2013.997) and the corresponding classification; alignment was assessed with latent transition analysis (entropy =\u2002.96). The procedure was integrated into an application supporting administration, scoring, and reporting (score, risk, profiles, graphs). Combining a cross-validated cutoff and clinically useful profiles with CAT and its automated administration and reporting application strengthens triage and personalization, enabling large-scale, multidomain screening and earlier intervention for hikikomori risk.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0355595","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:42:43Z","timestamp":1787247763000},"page":"e0355595","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine learning for hikikomori screening: The adaptive HRI-15"],"prefix":"10.1371","volume":"21","author":[{"given":"Daiana","family":"Colledani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2982-7178","authenticated-orcid":true,"given":"Pasquale","family":"Anselmi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4205-7393","authenticated-orcid":true,"given":"Lucia","family":"Monacis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Genetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniele","family":"Fassinato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis J.","family":"Gomez 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