{
  "id": 18088,
  "url": "https://www.jmir.org/2026/1/e86467",
  "title": "Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study",
  "summary": "Background: Health care systems generate vast amounts of unstructured text, such as clinical notes, which capture nuanced patient experiences, clinical reasoning, and subtle indicators of health status. While health system research has traditionally relied upon structured data, natural language processing (NLP) enables the extraction of this rich textual information. Leveraging NLP could improve the identification and characterization of underreported adverse drug events (ADEs). Objective: The p",
  "authors": "Alan Katz, Abhishek Dhankar, Gillian Fransoo, Diane Gordon Pappas, Amani F Hamad, Christine Leong, Alexander Singer",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T17:30:10.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18088",
  "original_url": "https://www.jmir.org/2026/1/e86467",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}