{
  "abstract": "BackgroundRelapsing-remitting multiple sclerosis (RRMS) often progresses to secondary-progressive MS (SPMS), but diagnosis is typically retrospective, delaying potential interventions. Existing rule-based and machine learning approaches often lack prospective validation and provide no uncertainty estimates, limiting clinical adoption.\n\nObjectiveTo prospectively evaluate a conformal prediction (CP)-enabled random forest model for reproducing clinician-recorded disease course labels in the Swedish Multiple Sclerosis Registry (SMSReg) and compare its classifications with the Lorscheider criteria.\n\nMethodsThe CP model was trained and calibrated on SMSReg-2022 (10,787 individuals, 85,461 hospital visits) and then validated on SMSReg-2025 (15,719 individuals, 45,077 hospital visits; not included in the SMSReg-2022 training/calibration/testing datasets). Performance was assessed at the hospital visit and individual levels and compared with the Lorscheider criteria.\n\nResultsThe model maintained good calibration on the prospective dataset. At the per-hospital visit-level, the model accuracies were 92% (RRMS) and 95% (SPMS). At the individual level, sensitivity was 98%, specificity 89%, and F1-score 0.75. With clinician-recorded labels as reference, the model showed higher sensitivity than the Lorscheider criteria (98% vs. 71%), more transition assignments (90% vs. 57%), and higher F1-score (0.76 vs. 0.69).\n\nConclusionThe CP-enabled model prospectively reproduced clinician-recorded disease course labels with calibrated uncertainty, supporting harmonization and decision support.",
  "abstract_shown": true,
  "affiliations": [
    "Uppsala University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "A. Parakkal Sreenivasan"
    },
    {
      "name": "Y. Noui"
    },
    {
      "name": "I. Erngren"
    },
    {
      "name": "O. Spjuth"
    },
    {
      "name": "J. Burman"
    },
    {
      "affs": [
        0
      ],
      "name": "K. Kultima"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d60qgm7ef8esq.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.26364939",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364939v1.full",
  "id": "d60qgm7ef8esq",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364939v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "neurology"
    ],
    [
      "openalex",
      "2734|Multiple Sclerosis Research Studies"
    ]
  ],
  "openalex": "W7221478786",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364939v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d60qgm7ef8esq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:37Z",
      "native_id": "10.64898/2026.10.07.26364939",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.07.26364939v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:neurology",
      "subfield": "neurology"
    }
  ],
  "title": "Prospective validation of an uncertainty-aware machine learning model for secondary progressive multiple sclerosis classification",
  "title_text": "Prospective validation of an uncertainty-aware machine learning model for secondary progressive multiple sclerosis classification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:37Z",
  "url": "https://research.draconitic.com/paper/d60qgm7ef8esq",
  "venue": "medRxiv",
  "versions": []
}