Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit

Abstract

D’Hondt et al. perform a qualitative and quantitative study on the implementation of machine learning (ML) in the intensive care unit (ICU). The authors interview hospital- and industry-based stakeholders to understand barriers in ML implementation and perform a number of ML experiments to quantify the impact of issues raised on model performance.