What question did this study set out to answer?

To develop a method for monitoring sleep trajectories in neurorehabilitation patients using clinical data and nursing notes.

A Sequence Clustering Approach to Mining Sleep Trajectories from Nursing Narratives and Structured Clinical Data

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This pipeline identifies sleep quality states using hierarchical clustering in neurorehabilitation patients, highlighting social determinants.

To develop a method for monitoring sleep trajectories in neurorehabilitation patients using clinical data and nursing notes.
Extracted and categorized 17,039 nighttime nursing annotations into sleep quality states.
Used a random forest classifier for annotation classification achieving high sensitivity and specificity.
Employed hierarchical clustering to analyze sleep sequences and identify clusters among post-acute inpatients.
Four distinct sleep trajectory clusters were identified based on functional and socio-environmental profiles.
Clusters varied in functional independence and sleep quality, revealing complex interactions among these factors.
Non-parametric statistics were used to compare clusters across clinical variables.

García‐Rudolph et al. (2025) studied this question.

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This pipeline identifies sleep quality states using hierarchical clustering in neurorehabilitation patients, highlighting social determinants.