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AI Set to Predict Heart Attack and Stroke Risk

Soon, artificial intelligence (AI) could be leveraged in predicting a patient’s future risk of developing stroke or heart attack.

A team based at the University of Cambridge is creating a machine learning tool to help forecast people’s risk by checking their healthcare records.

This effort is owed to the joint financing from the Alan Turing Institute and the British Heart Foundation.

Researchers intend to utilize the long-term healthcare records of more than two million individuals in the United Kingdom in a bid to create the algorithm.

Currently, GPs and clinicians utilize risk calculators as a section of the ‘NHS Health Check’ in a bid to evaluate a patient’s risk of developing either circulatory or heart problems in 10 years.

However, such calculators factor in a patient’s health only at the moment it is used as opposed to including their family and medical history.

SEE MORE: Top 10 Ways Artificial Intelligence is Impacting Healthcare

The calculators also fail to consider how a given patient’s risk factors have transformed over the years or even distinguish the risk by specific circulatory and heart illnesses like heart failure, abnormal heart rhythms, strokes, or heart attacks.

The algorithm is expected to utilize a wealth of information on an individual’s long-term healthcare records; map previous trends in every patient’s health; separate and categorize the risk for a particular type of illness.

This will allow clinicians to diagnose and forecast not only a patient’s risk of illness but also treat them proactively as opposed to reactively.

The University of Cambridge’s Senior University Lecturer in Biostatistics, Dr. Angela Wood, claimed: “It’s only recently that we’ve had the technology to process the huge amount of data available in health records and use it to our own advantage. New algorithms could allow us to pick up entirely new and detailed patterns in people’s past health to predict their risk of future events – ultimately saving lives.”

The project is among six other research grant applications that were awarded via a £550,000 dedicated joint financing scheme established between The Alan Turing Institute and the British Heart Foundation (BHF).

The chosen projects also entail the use of machine learning in personalizing the risk posed by an array of factors such as high blood pressure and smoking to bolster the accuracy of treatment and intervention.

All the six projects will become part of the Alan Turing’s health research programme, which intends to not only expedite the scientific understanding of illness but also boost health via data-powered innovation in statistical science and artificial intelligence.

Associate Medical Director at BHF, Professor Metin Avkiran, said: “Investing in data science and machine learning innovation is critical if we want to reduce the burden of early deaths and unnecessarily suffering from heart and circulatory disease.”

“Data science is set to accelerate breakthroughs in medical research and the outcome of projects such as this could ultimately transform care for millions of people living under the shadow of heart and circulatory disease in the UK.”

Established in 1961, the British Heart Foundation (BHF) is a UK-based charity organization that is involved in funding research to fight circulatory and heart illnesses as well as their risk factors.

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KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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