Outputs (4)

Evaluating a clinical tool (FAMCAT) for identifying familial hypercholesterolaemia in primary care: a retrospective cohort study (2020)
Journal Article
Akyea, R. K., Qureshi, N., Kai, J., de Lusignan, S., Sherlock, J., McGee, C., & Weng, S. (2020). Evaluating a clinical tool (FAMCAT) for identifying familial hypercholesterolaemia in primary care: a retrospective cohort study. BJGP Open, 4(5), 1-10. https://doi.org/10.3399/bjgpopen20X101114

Background: Familial hypercholesterolaemia (FH) is an inherited lipid disorder causing premature heart disease, which is severely underdiagnosed. Improving the identification of people with FH in primary care settings would help to reduce avoidable h... Read More about Evaluating a clinical tool (FAMCAT) for identifying familial hypercholesterolaemia in primary care: a retrospective cohort study.

Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care (2020)
Journal Article
Akyea, R. K., Qureshi, N., Kai, J., & Weng, S. F. (2020). Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care. npj Digital Medicine, 3(1), Article 142. https://doi.org/10.1038/s41746-020-00349-5

Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and de... Read More about Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care.

Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source (2020)
Journal Article
Ntaios, G., Weng, S. F., Perlepe, K., Akyea, R., Condon, L., Lambrou, D., …Michel, P. (2021). Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source. European Journal of Neurology, 28(1), 192-201. https://doi.org/10.1111/ene.14524

Background: Hierarchical clustering, a common “unsupervised” machine‐learning algorithm, is advantageous for exploring potential underlying aetiology in particularly heterogeneous diseases. We investigated potential embolic sources in ESUS using a da... Read More about Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source.

Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models (2020)
Journal Article
Akyea, R. K., Leonardi-Bee, J., Asselbergs, F. W., Patel, R. S., Durrington, P., Wierzbicki, A. S., …Weng, S. F. (2020). Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models. BMJ Open, 10(7), Article e034564. https://doi.org/10.1136/bmjopen-2019-034564

Introduction: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. With advances in early diagnosis and treatment of CVD and increasing life expectancy, more people are surviving initial CVD events. However, models t... Read More about Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models.