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.
From this model, a risk score was derived that determined low and high risk of death by identifying eight readily available variables collected for the majority of patients with heart failure:Diastolic blood pressure, Creatinine, Blood urea nitrogen, White blood cell count, Platelets, Albumin and Red blood cell distribution.
Yagil said the newly developed model was able to accurately predict life expectancy 88 per cent of the time and performed substantially better than other popular published models.
"This tool gives us insight, for example, on the probability that a given patient will die from heart failure in the next three months or a year," said researcher Eric Adler.
"This is incredibly valuable. It allows us to make informed decisions based on a proven methodology and not have to look into a crystal ball," he added.
The tool was additionally tested using de-identified patient data from the University of California San Francisco and a data base derived from 11 European medical centers.
"It was successful in those cohorts as well," said Yagil.
"Being able to repurpose our findings in independent populations is of utmost importance, thus validating our methodology and its results," Yagil added.
Researchers said the partnership between physicists and cardiologists was critical to developing a reliable tool and extensive knowledge and experiences from both sides proved synergetic.
The study was published in the European Journal of Heart Failure.
( With inputs from IANS )