Artificial Intelligence methods for Improved Detection of undiagnosed Heart Failure with Preserved Ejection Fraction (HFpEF).
Hochung WuDhruva BiswasMatthew RyanBrett 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 (2023)
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. This article is protected by copyright. All rights reserved.
Keyphrases
- artificial intelligence
- machine learning
- big data
- end stage renal disease
- deep learning
- electronic health record
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- risk factors
- cardiovascular events
- cardiovascular disease
- type diabetes
- patient reported outcomes
- label free
- data analysis
- patient reported
- real time pcr