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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedHuda A, Castaño A, Niyogi A
A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.
Transthyretin 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. In this study, the investigators showed that a random forest machine learning model could identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data.
AHRQ-funded; HS026385.
Citation: Huda A, Castaño A, Niyogi A .
A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.
Nat Commun 2021 May 11;12(1):2725. doi: 10.1038/s41467-021-22876-9..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Neurological Disorders, Diagnostic Safety and Quality, Risk
Sangal RB, Fodeh S, Taylor A
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. In this study, the investigators aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.
AHRQ-funded; HS023554.
Citation: Sangal RB, Fodeh S, Taylor A .
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
J Stroke Cerebrovasc Dis 2020 Dec;29(12):105306. doi: 10.1016/j.jstrokecerebrovasdis.2020.105306..
Keywords: Cardiovascular Conditions, Neurological Disorders, Diagnostic Safety and Quality, Data