EP16: Finding missed cases of familial hypercholesterolemia in health by Juan M. Banda and Others

Disclaimer: This podcast is completely AI generated by ⁠⁠⁠NoteBookLM⁠⁠⁠ 🤖 Summary During this episode we talk about this research paper which describes the development and validation of a machine learning classifier that can be used to identify individuals with familial hypercholesterolemia (FH) using electronic health record (EHR) data. FH is a genetic condition that causes high cholesterol levels and significantly increases the risk of cardiovascular disease, but it is often underdiagnosed. The authors demonstrate that their classifier, trained on data from Stanford Health Care, achieves high positive predictive value and sensitivity, meaning it is effective at identifying patients with a high probability of FH. The classifier was further validated on an independent dataset from Geisinger Healthcare System, showing its ability to generalise to different healthcare settings. The authors believe this classifier could significantly improve the identification of FH patients and facilitate early intervention to reduce their risk of cardiovascular disease.

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