Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction.
Hochung WuDhruva BiswasMatthew RyanBrett S BernsteinMaleeha RizviNatalie FairhurstGeorge KayeRanu BaralTom SearleNarbeh MelikianDaniel SadoThomas F LüscherRichard Grocott-MasonGerald Carr-WhiteJames TeoRichard DobsonDaniel I BromageTheresa A McDonaghAjay M ShahKevin O'GallagherPublished in: European journal of heart failure (2024)
This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- end stage renal disease
- electronic health record
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- cardiovascular events
- risk factors
- type diabetes
- coronary artery disease
- cardiovascular disease
- clinical practice
- patient reported outcomes
- quantum dots
- sensitive detection