When Avi Yagil, PhD, Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012, he thought he had caught a cold from his travels. When a "collection of pills" did not improve his symptoms, his wife encouraged him to see a doctor. Further tests revealed something far more life-threatening to Yagil than the common cold. "A chest X-Ray showed my lungs were flooded with fluid, and a subsequent echocardiogram found I had damage to my heart." Yagil was diagnosed with heart failure.

Researchers have developed an artificial intelligence (AI) tool to predict life expectancy in heart failure patients. The machine learning algorithm based on de-identified electronic health, records data of 5,822 hospitalised or ambulatory patients with heart failure at UC San Diego Health in the US. "We wanted to develop a tool that predicted life expectancy in heart failure patients, there are apps where algorithms are finding out all kinds of things, like products you want to purchase," said Avi Yagil, Professor at University of California. "We needed a similar tool to make medical decisions. Predicting mortality is important in patients with heart failure. Current strategies for predicting risk, however, are only modestly successful and can be subjective," Yagil added.

When Avi Yagil, Ph.D., Distinguished Professor of Physics at University of California San Diego flew home from Europe in 2012, he thought he had caught a cold from his travels. When a "collection of pills" did not improve his symptoms, his wife encouraged him to see a doctor. Further tests revealed something far more life-threatening to Yagil than the common cold. "A chest X-Ray showed my lungs were flooded with fluid, and a subsequent echocardiogram found I had damage to my heart." Yagil was diagnosed with heart failure.