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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Health & Wellbeing — Mental Health & Psychological Support, Medical Devices & Health Technology, Health & Medical Research · Depth: Expert, quick

Summary

A critical analysis reveals that suicidality detection datasets derived from electronic health records (EHRs) encode specific, often unexamined, operationalizations of suicidality. This framing, which treats clinical documentation as ground truth, is shaped by factors such as data authorship, episode boundaries, and ambiguity resolution. A case study of the ScAN dataset, constructed from MIMIC-III clinical notes, demonstrates how governance constraints, ICD-based cohort selection, single-annotator labeling, and hospital-stay-level aggregation influence label generation. These processes prioritize clinician judgment, define suicidality as a bounded episode, and assume intent can be reliably inferred from documentation. Further linguistic analysis shows that identical labels can encompass diverse clinical framings, varying in temporality, negation, and uncertainty, with labeling patterns also differing based on insurance status. The findings emphasize the necessity for the clinical NLP community to scrutinize the inherent assumptions within suicidality datasets before accepting their labels as definitive ground truth.

Key takeaway

For NLP Engineers developing suicidality detection models from clinical text, you must critically evaluate the underlying dataset's construction. Recognize that EHR-derived labels are not objective ground truth but reflect specific clinical operationalizations, influenced by factors like authorship and aggregation. Before deployment, analyze how governance, cohort selection, and annotation practices might introduce biases or obscure label heterogeneity, especially across patient demographics. Your models' reliability hinges on understanding these embedded assumptions, preventing misinterpretations of clinical intent.

Key insights

EHR-based suicidality datasets embed specific, unexamined assumptions about suicidality, impacting NLP model reliability.

Principles

• Dataset construction choices shape label meaning.
• Clinician judgment is foregrounded in EHR labels.
• Identical labels can hide heterogeneous clinical framings.

In practice

• Examine dataset governance constraints.
• Analyze label heterogeneity across demographics.
• Scrutinize assumptions in "ground truth" labels.

Topics

• Clinical NLP
• Suicidality Detection
• EHR Datasets
• Dataset Bias
• MIMIC-III
• Labeling Practices

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.