Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): CHU de Rennes Aims Patients presenting heart failure (HF) symptoms with preserved left ventricular ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large University Hospital Center using machine learning. Methods and results We explored the use of clinical variables from electronic health records (EHR) in addition to echocardiography to identify different phenotypes of patients with heart failure and preserved ejection fraction. The proposed methodology identifies 4 phenotypic clusters based on both clinical and echocardiographic characteristics which have differing prognoses (death and cardio-vascular hospitalization). Conclusion This work demonstrated that AI derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

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