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Updated: Jan 17, 2026

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

Emir Veledar¹, Lili Zhou¹, Omar Veledar²

¹University of Miami Miller School of Medicine, Miami, Florida, United States of America.

|September 18, 2025

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This summary is machine-generated.

Explainable Machine Learning (XML) models identified non-clinical factors, including Social Drivers of Health (SDOH), as crucial predictors of stroke outcomes. Integrating these factors improves prediction accuracy for post-hospital care and risk identification.

Area of Science:

Utilizes advanced Explainable Machine Learning (XML) methodologies for complex health data analysis.
Focuses on predictive modeling in stroke care and patient outcomes.
Integrates clinical and non-clinical factors for comprehensive health assessments.

Background:

Stroke poses a significant global health burden, leading to high mortality and rehospitalization rates.
Traditional statistical models struggle with complex, multi-dimensional post-stroke data.
Improved prediction of post-hospitalization outcomes is essential for healthcare resource allocation.

Purpose of the Study:

To define an expanded list of clinical and non-clinical predictors for post-stroke outcomes.
To leverage Explainable Machine Learning (XML) models to enhance traditional prediction methods.
To improve the accuracy of predicting mortality and rehospitalization in stroke survivors.

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Main Methods:

Evaluated 11 established XML models to predict 90-day mortality and rehospitalization.
Analyzed data from 1,300 post-stroke individuals in the Transitions of Care Stroke Disparities Study (TCSD-S).
Incorporated clinical data (e.g., stroke severity) and non-clinical factors, including Social Drivers of Health (SDOH).

Main Results:

Identified 38 significant predictors, with 20 being non-clinical variables (SDOH, environmental, behavioral).
Non-clinical factors significantly enhanced prediction accuracy for stroke outcomes.
Secondary analysis in ischemic stroke patients confirmed the model's robustness and predictive performance.

Conclusions:

Integrating SDOH, environmental, and behavioral factors with clinical predictors boosts post-stroke outcome model accuracy.
Addressing socioeconomic disparities is critical during post-stroke transitions of care.
XML models effectively identify diverse predictors, guiding recovery and potentially aiding pre-stroke risk identification.