{
  "abstract": "Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables collaborative model development without transferring sensitive patient data, but its application in healthcare remains challenging because datasets often differ in size, population characteristics, and outcome definitions. In this study, we present a federated deep learning approach for privacy-preserving cardiovascular disease risk prediction that integrates two population-based cohorts with different characteristics: Lifelines, including 148,230 participants meeting the study inclusion criteria with self-reported outcomes, and the Rotterdam Study, including a smaller cohort of 10,155 participants with digitally linked clinical outcomes. Model performance was primarily evaluated on the Rotterdam Study because of its complete follow-up. Deep survival models trained using federated learning achieved a higher mean C-statistic than models trained locally without federation. For the Rotterdam Study, the C-statistic increased from 0.723 (95% CI: 0.714–0.732) to 0.737 (95% CI: 0.727–0.747). For Lifelines, the C-statistic increased from 0.791 (95% CI: 0.783–0.799) to 0.795 (95% CI: 0.785–0.804). These findings suggest that federated deep learning across heterogeneous cohorts can improve the discrimination of cardiovascular disease risk prediction models while keeping individual-level patient data within the participating institutions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands",
    "Netherlands eScience Center, Matrix THREE, Amsterdam, Netherlands",
    "Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands",
    "Department of Cardiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands",
    "Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hyunho Mo"
    },
    {
      "affs": [
        1
      ],
      "name": "Djura Smits"
    },
    {
      "affs": [
        0
      ],
      "name": "Mahlet A. Birhanu"
    },
    {
      "affs": [
        0
      ],
      "name": "Maarten J. G. Leening"
    },
    {
      "affs": [
        2
      ],
      "name": "Maarten J. G. Leening"
    },
    {
      "affs": [
        3
      ],
      "name": "Maarten J. G. Leening"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniel Bos"
    },
    {
      "affs": [
        2
      ],
      "name": "Daniel Bos"
    },
    {
      "affs": [
        4
      ],
      "name": "Pim van der Harst"
    },
    {
      "affs": [
        0
      ],
      "name": "Esther E. Bron"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3mjwt4qe6bm7r.bib",
  "cited_by": 0,
  "doi": "10.3389/fams.2026.1923866",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fams.2026.1923866/full",
  "id": "3mjwt4qe6bm7r",
  "issns": [
    "2297-4687"
  ],
  "landing_url": "https://doi.org/10.3389/fams.2026.1923866",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA273-280"
    ],
    [
      "lcc",
      "T57-57.97"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7167910421",
  "pdf_url": "https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2026.1923866/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/3mjwt4qe6bm7r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:48Z",
      "native_id": "ebcdf2f0f82449c1893f6bb329e3b61c",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fams.2026.1923866"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA273-280",
      "subfield": "statistics"
    }
  ],
  "title": "Federated deep learning for privacy-preserving cardiovascular disease risk prediction",
  "title_text": "Federated deep learning for privacy-preserving cardiovascular disease risk prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:48Z",
  "url": "https://research.draconitic.com/paper/3mjwt4qe6bm7r",
  "venue": "Frontiers in Applied Mathematics and Statistics",
  "versions": []
}