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Machine learning predicts 1-year mortality using EHR data

University of Minnesota researchers have developed a machine learning algorithm using electronic health record data to improve care delivery for seriously ill patients by accurately predicting the risk of 1-year mortality.

The random forest (RF) model, which estimates the risk of death within a year of the last day of hospitalization, leverages commonly obtained EHR data such as vital signs, complete blood count, basic and complete metabolic panel, demographic information, as well as ICD codes.

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