AI News, Can machine-learning improve cardiovascular risk prediction using routine clinical data?

<?xml version="1.0" encoding="UTF-8"?>Can machine-learning improve cardiovascular risk prediction using routine clinical data?

In 2012, there were 17.5 million deaths from CVD with 7.4 million deaths due to coronary heart disease (CHD) and 6.7 million deaths due to stroke [1].

Established approaches to CVD risk assessment, such as that recommended by the American Heart Association/American College of Cardiology (ACC/AHA), predict future risk of CVD based on well-established risk factors such as hypertension, cholesterol, age, smoking, and diabetes.

There remain a large number of individuals at risk of CVD who fail to be identified by these tools, while some individuals not at risk are given preventive treatment unnecessarily.

The aim of this study was to evaluate whether machine-learning can improve accuracy of cardiovascular risk prediction within a large general primary care population.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

In 2012, there were 17.5 million deaths from CVD with 7.4 million deaths due to coronary heart disease (CHD) and 6.7 million deaths due to stroke [1].

Established approaches to CVD risk assessment, such as that recommended by the American Heart Association/American College of Cardiology (ACC/AHA), predict future risk of CVD based on well-established risk factors such as hypertension, cholesterol, age, smoking, and diabetes.

There remain a large number of individuals at risk of CVD who fail to be identified by these tools, while some individuals not at risk are given preventive treatment unnecessarily.

The aim of this study was to evaluate whether machine-learning can improve accuracy of cardiovascular risk prediction within a large general primary care population.