Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.