{
  "id": 8506,
  "url": "https://doi.org/10.1371/journal.pone.0213653",
  "title": "Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants",
  "summary": "BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them.",
  "authors": "Ahmed M. Alaa, Thomas Bolton, Emanuele Di Angelantonio, James H.F. Rudd, Mihaela van der Schaar",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": "uk",
  "published_at": "2019-05-15T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:39.869Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/8506",
  "original_url": "https://doi.org/10.1371/journal.pone.0213653",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}