@inproceedings{au-2026-clinical,
    title = "Clinical Evidence and Patient Reviews: A Linked Dataset for Antidepressant Side Effects",
    author = "Au, Steven",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.52/",
    pages = "656--664",
    ISBN = "979-8-89176-434-7",
    abstract = "Clinical sources and patient-authored reviews often describe antidepressant side effects in different ways, but these differences are rarely measured directly. We present ClinPeer-AE, a linked dataset for comparing side-effect evidence from PubMed, ClinicalTrials.gov, WebMD, and Drugs.com while preserving source identity. Across five widely prescribed antidepressants, we find low overlap between clinical and peer sources, large differences in relative emphasis, and evidence that many peer-only effects also appear in U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) reports. These findings suggest that patient reviews provide useful context about recurring medication experiences and offer a complementary view of how side effects are described outside formal clinical settings."
}