@inproceedings{garg-etal-2026-labels,
    title = "Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text",
    author = "Garg, Priyanshi  and
      Rao, Ishita  and
      Ding, Jieqiong  and
      Paullada, Amandalynne",
    editor = "Zirikly, Aya  and
      Bar, Kfir  and
      MacAvaney, Sean  and
      Ireland, Molly  and
      Ophir, Yaakov  and
      Atzil-Slonim, Dana  and
      Varadarajan, Vasudha  and
      Bedrick, Steven  and
      Desmet, Bart",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.clpsych-1.9/",
    pages = "119--127",
    ISBN = "979-8-89176-421-7",
    abstract = "Clinical NLP increasingly relies on electronic health record (EHR) datato detect suicidal behaviors, treating clinical documentation as morereliable ground truth than social media. We argue that this framingobscures how EHR-based suicidality datasets encode a particularoperationalization of suicidality, shaped by who authors the data,how episodes are bounded, and how ambiguity is resolved. We groundthis argument in a case study of the ScAN dataset,built over MIMIC-III clinical notes. We show how governanceconstraints, ICD-based cohort selection, single-annotator labeling,and hospital-stay-level aggregation produce labels that foregroundclinician judgment, treat suicidality as a bounded episode, andassume that intent can be reliably inferred from documentation. Alinguistic analysis demonstrates that identical labels subsumeheterogeneous clinical framings differing in temporality, negation,and uncertainty, and that labeling patterns differ across insurancestatus. We argue the clinical NLP community should examine theassumptions embedded in suicidality datasets before interpretingtheir labels as ground truth."
}