Machine Learning in Healthcare: Two Case Studies

Abstract

Machine learning (ML) is making significant inroads into the field of medicine. It can be used as a preventative measure by predicting a patient’s diagnosis and introducing early treatment to prevent adverse outcomes or lessen their impact. However, despite many demonstrated advantages of machine learning tools in health-care, their performance assessment remains partial at best. In particular, human interactions with machine learning tools in clinical settings remain poorly researched. This review examined machine learning tools in two important areas, sepsis diagnosis and suicide prediction. However, our exploration into the use of machine learning in sepsis and suicide prediction turned up no thorough human factors analyses of provider interactions with their machine learning tools, suggesting a critical research gap waiting to be filled.