Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text

arXiv:2606.19637v1 Announce Type: new Abstract: Clinical NLP increasingly relies on electronic health record (EHR) data to detect suicidal behaviors, treating clinical documentation as more reliable ground truth than social media. We argue that this framing obscures how EHR-based suicidality datasets encode a particular operationalization of suicidality, shaped by who authors the data, how episodes are bounded, and how ambiguity is resolved. We ground this argument in a case study of the ScAN dataset, built over MIMIC-III clinical notes. We show how governance constraints, ICD-based cohort sel

Source: arXiv cs.CL — read the full report at the original publisher.

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