Identifying determinants of readmission and death post-stroke using explainable machine learning

Abstract

Background Stroke remains a global health challenge with high rates of mortality and rehospitalization placing significant demands on healthcare systems. Identifying factors that determine outcomes of post-hospitalization improves resource allocation. Traditional statistical prediction models are suboptimal for the analysis of complex, multidimensional datasets. The objective of our study is to define the extended list of clinical and non-clinical predictors, which we believe can be achieved using Explainable Machine Learning (XML) models as an expansion of conventional methods. Methods We evaluated 11 established XML models that represent key ML methodologies to predict 90-day outcomes, namely mortality and rehospitalization among stroke survivors. The study population are 1,300 post-stroke individuals enrolled in the Transitions of Care Stroke Disparities Study (TCSD-S) (NIH/NIMH, NCT03452813) between June 2018 – October 2022. The care after transition data is sourced from participating comprehensive stroke centers and from the Florida Stroke Registry. The analysis incorporated clinical (e.g., age, stroke severity, comorbidities) and nonclinical factors including Social Drivers of Health (SDOH). A combined ranking approach, using Weighted Importance Scores and Frequency Counts, identified significant predictors across models.