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A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
2021
Nature Communications
AbstractTransthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine
doi:10.1038/s41467-021-22876-9
pmid:33976166
pmcid:PMC8113237
fatcat:nebdhmuzfzakdbmc5savb7iv3m