After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual's social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

ADAPT Research Centre School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

AIDHM Artificial Intelligence in Digital Health and Medicine Technological University Dublin Dublin Ireland

Fundació Institute d'Investigació en Ciències de la Salut Germans Trias i Pujol Badalona Spain

Institut Guttmann Hospital de Neurorehabilitacio Badalona Spain

RESQ Comprehensive solutions of healthcare improvement based on the global Registry of Stroke Care Quality Horizon Europe Project Brno Czech Republic

School of Computer Science University College Dublin Dublin Ireland

STRATIF AI Continuous stratification for improved prevention treatment and rehabilitation of stroke patients using digital twins and AI Horizon Europe Project Linköping Sweden

Universitat Autònoma de Barcelona Cerdanyola del Vallès Bellaterra Spain

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