Predicting Post-stroke Hospital Discharge Disposition Using Interpretable Machine Learning Approaches

Stroke is the fifth leading cause of death for Americans. Due to critical consequences and cost, an efficient stroke system of care (i.e., acute treatment, post-stroke acute rehabilitation) is necessary. Early determination of hospital discharge disposition is important for stroke management, and can make an immense impact in optimizing acute treatment and planning post-acute rehabilitation with desired outcomes. With the rise of sophisticated machine learning models, many researchers have gained momentum in using such models in their studies. However, due to the lack of explanations that models provide, the integrity of the prediction result is often challenged. Our goal is to predict post-stroke hospital discharge disposition using machine learning models to increase the prediction capability while providing explanations for the results using an interpretation method, such as Local Interpretable Model-agnoistic Explanations (LIME). Our results demonstrate the effectiveness of LIME in providing interpretability to machine learning models and suggests further exploration in performance improvement.