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

Sleep quality critically influences recovery in neurological patients, yet its longitudinal monitoring during hospitalization remains limited. Nursing narrative notes offer an underutilized resource to track sleep trajectories objectively across time.To propose and apply a formal pipeline that integrates structured clinical data and unstructured nursing annotations to monitor sleep trajectories during post-acute inpatient neurorehabilitation, relying exclusively on free-to-use software tools and without increasing nursing workload.A total of 17,039 nighttime nursing annotations were extracted and categorized into four sleep quality states. Two expert raters manually labeled a training set of 2,000 annotations (κ = 0.84). A random forest classifier achieved 0.93 sensitivity and 0.94 specificity and was used to classify the remaining notes. Sleep sequences were constructed and clustered using sequence analysis (TraMineR) and hierarchical clustering (AGNES, Ward's method). The obtained clusters (silhouette = 0.40) were compared using non-parametric statistics across clinical, functional, and social variables in a cohort of 303 post-acute consecutive neurorehabilitation inpatients.Four distinct sleep trajectory clusters were identified, each characterized by unique functional and socio-environmental profiles. The first group (<i>n</i> = 102; 33.7%) combined high functional independence, strong social support, stable economy, short hospitalization, and favorable sleep quality. The second group (<i>n</i> = 76; 25.1%) presented moderate functional independence, precarious economic conditions, and the highest proportion of poor sleep quality. The third group (<i>n</i> = 76; 25.1%) exhibited severe functional impairment, long hospitalization, poor housing conditions, but paradoxically the highest proportion of good sleep quality. The fourth group (<i>n</i> = 49; 16.2%) showed profound disability, relatively favorable socio-economic conditions, and predominance of intermediate sleep quality, likely influenced by medication. Distinctive sets of social and functional keywords emerged for each cluster.This pipeline identified clinically meaningful sleep profiles from nursing notes, highlighting functional and social determinants' role in shaping neurorehabilitation sleep trajectories.