| Title: | Application of deep machine learning to predict cardiovascular outcomes. |
| Lead Institution: | London School of Hygiene and Tropical Medicine |
| Principal investigator: | Professor Taane Clark |
Application 54050
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About
Asymptomatic left ventricular dysfunction (ALVD) of the heart is present in up to 6% of the UK population. ALVD is associated with reduced quality of life and longevity, and is treatable when diagnosed. An inexpensive, noninvasive screening tool for ALVD would assist clinical making. An electrocardiogram (ECG) is a routine test to measure the heart's electrical activity. By applying a machine learning approach to ECG data from the UK Biobank it will be possible to develop a model that predicts ALVD, as well as participant characteristics such as age and gender. The performance of the model will we assessed on ECG data from other populations. Ultimately, the validation of such approaches to predict cardiovascular outcomes could assist clinical decision making and public health initiatives. The duration of the project is twelve months.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 10864 | Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes | Julian Libiseller-Egger (+8) | 2022 | Scientific Reports |
Enabling scientific discoveries that improve human health