{
  "id": 8128,
  "url": "https://doi.org/10.1186/s12889-017-4914-3",
  "title": "A systematic review of data mining and machine learning for air pollution epidemiology",
  "summary": "BACKGROUND: Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology. METHODS: We conducted a systematic ",
  "authors": "Colin Bellinger, Mohomed Shazan Mohomed Jabbar, Osmar R. Zaı̈ane, Álvaro Osornio-Vargas",
  "category": "research",
  "topics": "healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2017-11-28T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:34.086Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/8128",
  "original_url": "https://doi.org/10.1186/s12889-017-4914-3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}