JERUSALEM, Sept. 29 (Xinhua) -- An Israeli-led research team has developed an artificial intelligence (AI) method to accurately predict whether a patient will develop atrial fibrillation (AF), the northern Israel Institute of Technology (Technion) said on Wednesday.
Atrial fibrillation is an abnormal heart rhythm that significantly increases patients' risk of stroke and death.
Its known risk factors include sedentary lifestyle, obesity, smoking, genetic predisposition and more.
In a study published in the European Heart Journal - Digital Health, Technion researchers and their colleagues from Brazil and Sweden wrote a machine learning algorithm that can capture patterns predictive of AF, even though no fibrillation diagnosed by a cardiologist at the time.
The team used more than one million ECG recordings from more than 400,000 patients to train a deep neural network to recognize patients at risk of developing AF within five years.
Then, they combined the deep neural network with clinical information about the patient, including risk factors.
The resulting machine learning model was able to correctly predict the development of AF risk in 60 percent of cases, with only 5 percent of persons identified as being potentially at risk did not develop the condition.
"It could easily be incorporated into clinical practice and improve healthcare management for many individuals", they concluded. Enditem
