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

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Health & Medical Research · Depth: Expert, quick

Summary

The paper "Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text" argues that relying on electronic health record (EHR) data for suicidality detection, while treating clinical documentation as ground truth, obscures inherent biases. It highlights how EHR-based suicidality datasets encode specific operationalizations of suicidality, influenced by data authorship, episode bounding, and ambiguity resolution. A case study of the ScAN dataset, built over MIMIC-III clinical notes, demonstrates that governance constraints, ICD-based cohort selection, single-annotator labeling, and hospital-stay-level aggregation produce labels reflecting clinician judgments and inferred intent. Linguistic analysis further reveals that identical labels can subsume heterogeneous clinical framings, differing in temporality, negation, and uncertainty. The authors advocate for clinical NLP to critically examine these embedded assumptions before interpreting dataset labels as objective ground truth.

Key takeaway

For NLP Engineers developing suicidality detection models using clinical text, recognize that your dataset's labels are not objective ground truth but reflect specific clinical operationalizations. Critically examine the dataset's construction, including annotation methods and how ambiguity was resolved, to understand embedded assumptions. This awareness is crucial for avoiding misinterpretation of model outputs and ensuring ethical, accurate clinical application.

Key insights

EHR-based suicidality datasets encode specific operationalizations of suicidality, shaped by construction choices, not objective ground truth.

Principles

• Dataset construction embeds specific operationalizations.
• Clinical documentation reflects clinician judgments.
• Identical labels can mask heterogeneous clinical framings.

In practice

• Examine dataset assumptions before label interpretation.
• Analyze label heterogeneity (temporality, negation).

Topics

• Clinical NLP
• Suicidality Detection
• EHR Data
• Dataset Construction
• MIMIC-III
• Ground Truth
• Linguistic Analysis

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.