How Machine Learning Can Improve Clinical Decision-Making

New research from a collaborative team of Duke investigators, including Duke School of Medicine, Duke Institute for Health Innovation, and the Duke Center for Healthcare Safety and Quality, looks at the ability of machine learning to identify mortality risk among hospitalized patients.

Why is this important? From the abstract:

The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-hospital death are both broadly applicable to all adult patients across a health system and readily implementable.

The results from this paper are promising and suggest that the model developed could be used at a system-wide level.

Read the Paper in JAMA Open Network