{
  "abstract": "Background: Although direct-acting antivirals have transformed hepatitis C virus (HCV) treatment, 25-30% of active infections in Spain remain undiagnosed. We aimed to develop and validate an interpretable machine-learning model to identify undiagnosed HCV infection using only data already recorded within the health system. Materials and Methods: We conducted a retrospective, population-based cohort study using the Andalusian Population Health Database (BPS). Individuals diagnosed with HCV between 2017 and 2024 (n=25,028) were matched, up to 10:1, to controls by birth year, sex, and health district. An Explainable Boosting Machine was trained on 2017-2022 data, with a threshold set in a 2023 validation set to guarantee [≥]60% sensitivity, then evaluated unmodified in an independent, non-matched, population-representative 2024 sample approximating real-world screening. Predictors comprised chronic-disease diagnoses and laboratory summary statistics recorded [≥]30 days before the index date; age and sex were excluded as matching variables. Predictors reflecting socioeconomic proxies or matching artefacts (pharmaceutical co-payment category, a zone-level deprivation index) were removed after explainability review. Results: In the independent test set, the model achieved an AUC of 0.837 (0.826 in validation), sensitivity 63.5%, specificity 91.8%, positive predictive value 7.0%, and negative predictive value 99.6%. Leading predictors were minimum alanine aminotransferase, HIV infection, dependence on non-tobacco substances, and platelet-count statistics, forming a clinically coherent, laboratory- and comorbidity-driven profile. Conclusion: An explainable model built exclusively from routine electronic health record data can identify undiagnosed HCV infection with discrimination comparable to previously published tools, evaluated under conditions approximating true population prevalence. Bias-auditing was feasible without sacrificing interpretability, supporting a scalable, equity-conscious instrument for the final phase of HCV elimination.",
  "abstract_shown": true,
  "affiliations": [
    "BarcelonaBeta Brain Research Center"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "A. Esteban-Medina"
    },
    {
      "name": "C. Loucera"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Dopazo"
    },
    {
      "name": "M. Romero Gomez"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/atacg5dbex5mh.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.05.26367298",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.11.05.26367298v1.full",
  "id": "atacg5dbex5mh",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.11.05.26367298v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.11.05.26367298v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/atacg5dbex5mh.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T00:20:58Z",
      "native_id": "10.64898/2026.11.05.26367298",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.11.05.26367298v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "An explainable, bias-audited machine learning model to detect hidden hepatitis C from region-wide real-world health records",
  "title_text": "An explainable, bias-audited machine learning model to detect hidden hepatitis C from region-wide real-world health records",
  "type": "preprint",
  "updated_at": "2026-11-08T00:20:58Z",
  "url": "https://research.draconitic.com/paper/atacg5dbex5mh",
  "venue": "medRxiv",
  "versions": []
}