Leeds Researchers Train AI Algorithm for Early Detection of Heart Failure

Leeds University researchers have trained an AI algorithm known as ‘Find-HF’ to detect early symptoms of heart failure by leveraging patient records. The researchers have stated that patients could be treated earlier by using AI to identify the risk.

As per reports, researchers have used the patient records of 565,284 UK adults to train the AI algorithm, and the British Heart Foundation (BHF) has funded the study. It was further tested on a database of 106,026 records from the Taiwan National University Hospital.

Consequently, the AI was able to accurately predict the patients at the highest risk of developing heart failure and those who could be admitted to the hospital with the condition within five years, the researchers said.

According to the BHF, there are currently more than one million people in the UK with heart failure.

Prof. Chris Gale, from Leeds Teaching Hospitals NHS Trust and the University of Leeds, said the technology would open a "crucial window of opportunity" for patients.

Prof. Gale, a consultant cardiologist, said: "This is an extremely powerful and unique national resource, and it is time to use these data to benefit patients.

“Find-HF could potentially bring diagnoses forward by two years," he added.

Dr. Ramesh Nadarajah, a health data research UK fellow at the University of Leeds, said: "Many people receive their diagnosis of heart failure at too late a stage when disease-modifying treatments are potentially less effective, especially women and older people.

"We are using machine learning tools with routinely collected data to identify people with heart failure earlier so that they can get the right treatment, prevent hospital admissions and deaths, and improve quality of life."