Artificial intelligence using ECG criteria could assist diagnosis of HFpEF

Individuals with undiagnosed HF with preserved ejection fraction represent an at-risk group whose diagnosis could be improved by artificial intelligence trained with electronic health record data, according to researchers in the U.K.
“Given that HFpEF prediction scores and diagnostic criteria are relatively recent developments, it is probable that there are many patients with a prior diagnosis of HF where the formal diagnosis of HFpEF has not been made,” Jack Wu, research platform engineer at the School of Cardiovascular and Metabolic Medicine & Sciences, British Heart

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