ArticleSleep medicine2026
Expert annotation of free-text descriptions of dream enactment behaviours associated with REM sleep behaviour disorder.
Article in Sleep medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We developed a database of free-text descriptions of dream enactment behaviours (DEBs) from unstructured clinical notes of United States Veterans with computationally phenotyped diagnoses of rapid eye movement sleep behaviour disorder (RBD). We additionally examined characteristics of DEBs considered essential for RBD diagnosis according to expert consensus. Free-text DEB descriptions were manually extracted from the clinical records of 200 randomly sampled Veterans; RBD computational diagnoses were defined as ≥2 ICD codes for RBD (>30 days but <390 days apart) and procedural codes for in-lab polysomnography within 6 months. DEB descriptions were categorized by clinical features and labeled as essential or not through expert consensus. The proportions of essential versus non-essential DEB types were compared with Chi-Square of Fisher's exact tests. Out of 195 unique DEB descriptions, experts labeled 124 (63.6 %) as essential RBD symptomatology. These included variations of "dream enactment behaviour" (e.g., "acted out dreams"); hitting, punching, or kicking bedpartners, pets, and/or bedside objects; and other mentions of sleep-related violence. Non-essential descriptions mentioned sleep-talking, thrashing, vivid dreams, nightmares, and sleeping in separate beds. Descriptions of sleep-related movements (n = 119) were more likely labeled as essential (75.6 %) versus non-essential (24.4 %; p = 0.00). Potentially violent or injurious movements were more likely labeled as essential (91.4 %) versus non-essential (8.6 %; p = 0.00). Expert labeling emphasized the significance of sleep-related movements and potentially injurious behaviours for RBD diagnosis in free-text medical records. A natural language processing algorithm that detects DEBs from free-text could further characterize sleep-related violence and injury risks associated with DEBs according to demographics and comorbidities.
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