{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:12:20Z","timestamp":1771333940586,"version":"3.50.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018693","name":"Horizon Europe","doi-asserted-by":"publisher","award":["101017598"],"award-info":[{"award-number":["101017598"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Medical Informatics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.ijmedinf.2026.106295","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T07:50:45Z","timestamp":1768981845000},"page":"106295","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["The association of environmental exposure with multiple sclerosis severity score: A study based on sequential data modeling"],"prefix":"10.1016","volume":"210","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0506-306X","authenticated-orcid":false,"given":"Mahin","family":"Vazifehdan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1431-6044","authenticated-orcid":false,"given":"Pietro","family":"Bosoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6188-6413","authenticated-orcid":false,"given":"Erica","family":"Tavazzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrico","family":"Longato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eleonora","family":"Tavazzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Bergamaschi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8415-4688","authenticated-orcid":false,"given":"Barbara","family":"Di Camillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4324-7954","authenticated-orcid":false,"given":"Ameen","family":"Abu-Hanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6974-9808","authenticated-orcid":false,"given":"Riccardo","family":"Bellazzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arianna","family":"Dagliati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.ijmedinf.2026.106295_bib0005","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1016\/j.jval.2022.03.010","article-title":"Multiple sclerosis progressive courses: a clinical cohort long-term disability progression study","volume":"25","author":"Cortesi","year":"2022","journal-title":"Value Health"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2025.108624","article-title":"Explainable time-to-progression predictions in multiple sclerosis","volume":"263","author":"D\u2019hondt","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106295_bib0020","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-18755-8","article-title":"The geographic association of multiple sclerosis and amyotrophic lateral sclerosis","volume":"15","author":"Schilling","year":"2025","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106295_bib0025","doi-asserted-by":"crossref","first-page":"749","DOI":"10.3390\/mi14040749","article-title":"A deep learning approach for predicting multiple sclerosis","volume":"14","author":"Ponce de Leon-Sanchez","year":"2023","journal-title":"Micromachines"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0030","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1038\/s41591-025-03901-6","article-title":"Ai-driven reclassification of multiple sclerosis progression","volume":"31","author":"Ganjgahi","year":"2025","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0035","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.1093\/brain\/awac016","article-title":"How patients with multiple sclerosis acquire disability","volume":"145","author":"Lublin","year":"2022","journal-title":"Brain: J. Neurol."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0040","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/S1474-4422(22)00289-7","article-title":"Multiple sclerosis progression: time for a new mechanism-driven framework","volume":"31","author":"Kuhlmann","year":"2023","journal-title":"The Lancet Neurology"},{"issue":"11","key":"10.1016\/j.ijmedinf.2026.106295_bib0045","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1212\/WNL.33.11.1444","article-title":"Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (edss)","volume":"33","author":"Kurtzke","year":"1983","journal-title":"Neurology"},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106295_bib0050","doi-asserted-by":"crossref","first-page":"82","DOI":"10.4274\/csmedj.galenos.2022.2022-11-11","article-title":"Expanded disability status scale (edss) in multiple sclerosis","volume":"2","author":"Demir","year":"2022","journal-title":"Cam. Sak. Med. J."},{"issue":"2","key":"10.1016\/j.ijmedinf.2026.106295_bib0055","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1212\/WNL.49.2.378","article-title":"Genetic variation in the tumor necrosis factor alpha gene and the outcome of multiple sclerosis","volume":"49","author":"Weinshenker","year":"1997","journal-title":"Neurology"},{"issue":"1\u20132","key":"10.1016\/j.ijmedinf.2026.106295_bib0060","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/S0165-5728(01)00481-7","article-title":"Identifying disease modifying genes in multiple sclerosis","volume":"123","author":"Kantarci","year":"2002","journal-title":"J. Neuroimmunol."},{"issue":"7","key":"10.1016\/j.ijmedinf.2026.106295_bib0065","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1212\/01.WNL.0000156155.19270.F8","article-title":"Multiple sclerosis severity score: using disability and disease duration to rate disease severity","volume":"64","author":"Roxburgh","year":"2005","journal-title":"Neurology"},{"issue":"13","key":"10.1016\/j.ijmedinf.2026.106295_bib0070","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1177\/1352458519881994","article-title":"Redefining the multiple sclerosis severity score (msss): the effect of sex and onset phenotype","volume":"26","author":"Zhou","year":"2020","journal-title":"Mult. Scler. J."},{"issue":"7","key":"10.1016\/j.ijmedinf.2026.106295_bib0075","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1097\/RLI.0000000000000854","article-title":"A deep learning approach to predicting disease progression in multiple sclerosis using magnetic resonance imaging","volume":"57","author":"Storelli","year":"2022","journal-title":"Invest. Radiol."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0080","article-title":"Prediction of expanded disability status scale in patients with MS using deep learning","volume":"182","author":"Kabir","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0085","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1268860","article-title":"Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity","volume":"17","author":"Chen","year":"2024","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0090","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1177\/1352458517726866","article-title":"AIR pollution is associated to the multiple sclerosis inflammatory activity as measured by brain MRI","volume":"24","author":"Bergamaschi","year":"2018","journal-title":"Mult. Scler. J."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0095","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12940-020-0565-4","article-title":"Road proximity, AIR pollution, noise, green space and neurologic disease incidence: a population-based cohort study","volume":"19","author":"Yuchi","year":"2020","journal-title":"Environ. Health"},{"issue":"11","key":"10.1016\/j.ijmedinf.2026.106295_bib0100","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.3390\/ijerph18115962","article-title":"Heat exposure and multiple sclerosis\u2014a regional and temporal analysis","volume":"18","author":"Chacko","year":"2021","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0105","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.envres.2018.01.040","article-title":"Ozone, no2 and pm10 are associated with the occurrence of multiple sclerosis relapses. Evidence from seasonal multi-pollutant analyses","volume":"163","author":"Jeanjean","year":"2018","journal-title":"Environ. Res."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0110","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.envres.2017.03.049","article-title":"AIR pollution by particulate matter pm10 may trigger multiple sclerosis relapses","volume":"156","author":"Roux","year":"2017","journal-title":"Environ. Res."},{"issue":"6","key":"10.1016\/j.ijmedinf.2026.106295_bib0115","first-page":"48","article-title":"Model for prediction of progression in multiple sclerosis","volume":"5","author":"Pruenza","year":"2019","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"issue":"18","key":"10.1016\/j.ijmedinf.2026.106295_bib0120","doi-asserted-by":"crossref","DOI":"10.3390\/ijms231810651","article-title":"Evaluation of disability progression in multiple sclerosis via magnetic-resonance-based deep learning techniques","volume":"23","author":"Taloni","year":"2022","journal-title":"Int. J. Mol. Sci."},{"issue":"3","key":"10.1016\/j.ijmedinf.2026.106295_bib0125","doi-asserted-by":"crossref","first-page":"e272","DOI":"10.1111\/aos.15722","article-title":"The macular retinal ganglion cell layer as a biomarker for diagnosis and prognosis in multiple sclerosis: a deep learning approach","volume":"102","author":"Montol\u00edo","year":"2024","journal-title":"Acta Ophthalmol."},{"issue":"1854","key":"10.1016\/j.ijmedinf.2026.106295_bib0130","article-title":"The brainteaser datasets: clinical, wearable and environmental data for als & MS progression modeling","volume":"12","author":"Faggioli","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0135","series-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction, Vol. 13390 of Lecture Notes in Computer Science","first-page":"395","article-title":"Intelligent disease progression prediction: overview of idpp@clef 2022","author":"Guazzo","year":"2022"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0140","series-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction: 14th International Conference of the CLEF Association, CLEF 2023, Thessaloniki, Greece, September 18\u201321, 2023, Proceedings, Vol. 14163 of Lecture Notes in Computer Science","first-page":"343","article-title":"Intelligent disease progression prediction: overview of idpp@clef 2023","author":"Faggioli","year":"2023"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0145","series-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction, Vol. 14959 of Lecture Notes in Computer Science","first-page":"118","article-title":"Intelligent disease progression prediction: overview of idpp@clef 2024","author":"Birolo","year":"2024"},{"issue":"17","key":"10.1016\/j.ijmedinf.2026.106295_bib0165","doi-asserted-by":"crossref","first-page":"9391","DOI":"10.1029\/2017JD028200","article-title":"An ensemble version of the e-obs temperature and precipitation data sets","volume":"123","author":"Cornes","year":"2018","journal-title":"J. Geophys. Res.: Atmos."},{"issue":"4","key":"10.1016\/j.ijmedinf.2026.106295_bib0170","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1076\/edre.7.4.353.8937","article-title":"A review of methods for missing data","volume":"7","author":"Pigott","year":"2001","journal-title":"Educ. Res. Eval."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0175","first-page":"349","article-title":"Assessing risk of bias due to missing results in a synthesis","author":"Page","year":"2019","journal-title":"Cochrane Handb. Syst. Rev. Interv."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0180","author":"Vazifehdan"},{"issue":"5","key":"10.1016\/j.ijmedinf.2026.106295_bib0185","doi-asserted-by":"crossref","first-page":"265","DOI":"10.7224\/1537-2073.2015-097","article-title":"Severity grading in multiple sclerosis: a proposal","volume":"18","author":"Charlson","year":"2016","journal-title":"Int. J. MS Care"},{"issue":"7","key":"10.1016\/j.ijmedinf.2026.106295_bib0190","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1016\/0098-3004(91)90093-S","article-title":"Memory intensive statistical algorithms for multibeam bathymetric data","volume":"17","author":"Ware","year":"1991","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0195","author":"Erickson"},{"issue":"10","key":"10.1016\/j.ijmedinf.2026.106295_bib0200","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","article-title":"Permutation importance: a corrected feature importance measure","volume":"26","author":"Altmann","year":"2010","journal-title":"Bioinformatics"},{"issue":"9","key":"10.1016\/j.ijmedinf.2026.106295_bib0205","doi-asserted-by":"crossref","first-page":"2736","DOI":"10.1111\/ene.15910","article-title":"Relapse-associated worsening in a real-life multiple sclerosis cohort: the role of age and pyramidal phenotype","volume":"30","author":"Zangh\u00ec","year":"2023","journal-title":"Eur. J. Neurol."},{"issue":"13","key":"10.1016\/j.ijmedinf.2026.106295_bib0210","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1177\/1352458516630396","article-title":"The relationship of age with the clinical phenotype in multiple sclerosis","volume":"22","author":"Scalfari","year":"2016","journal-title":"Mult. Scler. J."},{"issue":"14","key":"10.1016\/j.ijmedinf.2026.106295_bib0215","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1177\/1352458517690618","article-title":"Age related multiple sclerosis severity score: disability ranked by age","volume":"23","author":"Manouchehrinia","year":"2017","journal-title":"Mult. Scler. J."},{"issue":"20","key":"10.1016\/j.ijmedinf.2026.106295_bib0220","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1212\/WNL.0b013e3181c1e44f","article-title":"Impact of multiple sclerosis relapses on progression diminishes with time","volume":"73","author":"Tremlett","year":"2009","journal-title":"Neurology"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0225","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.envres.2015.11.017","article-title":"Effects of particulate matter exposure on multiple sclerosis hospital admission in lombardy region, Italy","volume":"145","author":"Angelici","year":"2016","journal-title":"Environ. Res."},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0230","first-page":"1","article-title":"Exploring the impact of ambient AIR PM2. 5 on multiple sclerosis: an experimental dive into neuroinflammation","author":"Mozaffari","year":"2025","journal-title":"Toxicol. Mech. Methods"},{"issue":"13","key":"10.1016\/j.ijmedinf.2026.106295_bib0235","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.3390\/nu15132978","article-title":"The role of vitamin d in neuroprotection in multiple sclerosis: an update","volume":"15","author":"Sangha","year":"2023","journal-title":"Nutrients"},{"key":"10.1016\/j.ijmedinf.2026.106295_bib0240","first-page":"1","article-title":"Time-dependent deep learning prediction of multiple sclerosis disability","author":"Mayfield","year":"2024","journal-title":"J. Imaging Inform. Med."},{"issue":"7","key":"10.1016\/j.ijmedinf.2026.106295_bib0245","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1177\/13524585241249422","article-title":"Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis","volume":"30","author":"Collorone","year":"2024","journal-title":"Mult. Scler. J."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106295_bib0250","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-78212-6","article-title":"Prediction of disease progression and outcomes in multiple sclerosis with machine learning","volume":"10","author":"Pinto","year":"2020","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.ijmedinf.2026.106295_bib0255","doi-asserted-by":"crossref","DOI":"10.1093\/braincomms\/fcae427","article-title":"Machine learning\u2013based prediction of disease progression in primary progressive multiple sclerosis","volume":"7","author":"Gurevich","year":"2025","journal-title":"Brain Commun."},{"issue":"22","key":"10.1016\/j.ijmedinf.2026.106295_bib0260","doi-asserted-by":"crossref","first-page":"12041","DOI":"10.1007\/s00500-022-07503-z","article-title":"Machine learning techniques for prediction of multiple sclerosis progression","volume":"26","author":"Branco","year":"2022","journal-title":"Soft Comput."}],"container-title":["International Journal of Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626000353?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626000353?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:20:49Z","timestamp":1771330849000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386505626000353"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":48,"alternative-id":["S1386505626000353"],"URL":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106295","relation":{},"ISSN":["1386-5056"],"issn-type":[{"value":"1386-5056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"The association of environmental exposure with multiple sclerosis severity score: A study based on sequential data modeling","name":"articletitle","label":"Article Title"},{"value":"International Journal of Medical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106295","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"106295"}}