Artificial intelligence methods to detect heart failure with preserved ejection fraction within electronic health records: an equitable disease detection model.

ABSTRACT:

Aims

Heart failure with preserved ejection fraction (HFpEF) accounts for approximately half of all heart failure cases, with high levels of morbidity and mortality. However, many patients who meet diagnostic criteria for HFpEF do not have a documented diagnosis, particularly in non-White populations where conventional risk scores may underestimate risk. Our aim was to develop and validate a diagnostic prediction model to detect HFpEF based on ESC criteria, AIM-HFpEF.

Methods and results

We applied natural language processing (NLP) and machine learning methods to routinely collected electronic health record (EHR) data from a tertiary centre hospital trust in London, UK, to derive the AIM-HFpEF model. We then externally validated the model and performed benchmarking against ex

SUBMITTER: Wu J 

PROVIDER: S-EPMC12821069 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature