Researchers have trained machine learning models using a large dataset to identify five subtypes of heart failure, which may improve risk prediction and treatment.

Researchers used patient zip codes to obtain their SDI, which was then entered into the risk calculator to determine how likely they would be hospitalized for heart failure within five years.