{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:23:30Z","timestamp":1779103410931,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"European Union \u2013 NextGenerationEU \/ MUR","award":["ECS00000035 - RAISE project"],"award-info":[{"award-number":["ECS00000035 - RAISE project"]}]},{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","award":["RF-2018-12366238"],"award-info":[{"award-number":["RF-2018-12366238"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007366","name":"Fondazione Italiana Sclerosi Multipla","doi-asserted-by":"publisher","award":["2013\/S3"],"award-info":[{"award-number":["2013\/S3"]}],"id":[{"id":"10.13039\/100007366","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of University and Research (MUR), Italy","award":["ECS00000035"],"award-info":[{"award-number":["ECS00000035"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Mult Scler"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Disability accrual in multiple sclerosis (MS) is highly variable and challenging to predict, complicating personalised care. Integrating machine learning (ML) with patient-reported outcomes (PROs) and clinician-assessed outcomes (CAOs) may support tailored interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>To develop and validate an interpretable ML model for predicting disability accrual trajectories in MS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A multicentre data set of 1,176 MS patients with up to 8\u2009years of follow-up was used. A random forest model was trained to predict disability accrual at 2, 3, 4, and 5\u2009years, using baseline clinical variables, PROs and initial risk class as predictors. Model performance was assessed using accuracy, area under the curve, and survival analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      437 patients composed final cohort. The model predicted disability changes with an accuracy of 0.82 (95% CI: 0.77\u20130.86) at 2\u2009years and 0.73 (95% CI: 0.66\u20130.80) at 5\u2009years. Initial risk class and baseline Expanded Disability Status Scale (EDSS) were the most influential predictors. Survival analysis confirmed model\u2019s ability to effectively capture the time-dependent patterns of disability accrual events at the population level (log rank\n                      <jats:italic toggle=\"yes\">p<\/jats:italic>\n                      \u2009&gt;\u2009.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>The model offers robust and interpretable predictions that may support clinical decision-making using routine clinical data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13524585251411093","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:31:30Z","timestamp":1769823090000},"page":"574-584","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Patient-reported outcomes as predictors of disability evolution in Multiple Sclerosis: An interpretable machine learning approach"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1552-0737","authenticated-orcid":false,"given":"Federica","family":"Di Antonio","sequence":"first","affiliation":[{"name":"NeuroBRITE Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy; 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Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4303-4297","authenticated-orcid":false,"given":"Alice","family":"Bollini","sequence":"additional","affiliation":[{"name":"NeuroBRITE Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6368-1833","authenticated-orcid":false,"given":"Margherita","family":"Monti Bragadin","sequence":"additional","affiliation":[{"name":"NeuroBRITE Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy; AISM Rehabilitation Service, Italian Multiple Sclerosis Society, Genoa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5756-0576","authenticated-orcid":false,"given":"Valeria","family":"Prada","sequence":"additional","affiliation":[{"name":"NeuroBRITE Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-9116","authenticated-orcid":false,"given":"Matteo","family":"Bauckneht","sequence":"additional","affiliation":[{"name":"Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7833-6095","authenticated-orcid":false,"given":"Silvia","family":"Morbelli","sequence":"additional","affiliation":[{"name":"Department of Medical Sciences, University of Turin, Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4094-9942","authenticated-orcid":false,"given":"Andrea","family":"Chincarini","sequence":"additional","affiliation":[{"name":"National Institute for Nuclear Physics (INFN), Genoa Section, Genoa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awl007"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.46.4.907"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1101\/cshperspect.a028928"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.12998\/wjcc.v3.i7.545"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10072-018-3589-6"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)31320-4"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2024.1407257"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/13524585251349354"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230219"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.13114.1"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11910-024-01354-x"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174866"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2024.105761"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103655"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1177\/13524585251347513"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.281053"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.33.11.1444"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/124.5.962"},{"key":"e_1_3_3_20_2","first-page":"4443","article-title":"A machine learning pipeline for multiple sclerosis course detection from clinical scales and patient reported outcomes","volume":"2015","author":"Fiorini S","year":"2015","unstructured":"Fiorini S, Verri A, Tacchino A, et al. 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