healthHealth and Medicine

Algorithm Predicts Which Heart Failure Patients Will Live Or Die Better Than We Can


Robin Andrews

Science & Policy Writer

This may help make heart transplantation efforts more efficient and accurate. Kate Vigdis/Shutterstock

Just in case you needed more convincing that artificial intelligence (AI) and machine learning are more life-saver than world-ender, a team at the University of California Los Angeles (UCLA) have been showing off their new toy: A computer program that’s able to predict whether people will survive heart failure, and for how long.

Heart failure is part of a suite of cardiovascular diseases that kill 17.7 million people every single year. According to the World Health Organization (WHO), that’s 31 percent of global deaths.


Heart failure describes a situation wherein the heart is unable to pump blood around the body properly, due to stiffness or weakness of some kind. It’s a long-term condition, and it tends to worsen over time. Sometimes, people require transplants to survive if it gets bad enough, but this depends on how likely the patient is to survive if they have one – a call that’s not exactly easy to make.

Making these literal life-or-death judgments is difficult even for medical professionals, which is where UCLA's algorithm, improved over an older version, comes into play.

The team from UCLA explain that, aside from more conventional methods of assessment of heart failure risk and cardiac transplantation, machine learning – which uses statistical techniques to allow software to act autonomously – has also been tested out in this regard before.

Writing in the journal PLOS One, the team explain that “existing clinical risk-scoring methods have suboptimal performance.” To wit, they’ve launched their Trees of Predictors (ToPs), an algorithm that uses 53 data points to predict how long people with heart failure will live, with or without a heart transplant, which you can play around with here.


Most of these points are associated with the potential recipients of a new heart; 14 apply to the donors, and six are linked to the compatibility between the two. Using machine learning, the algorithm was trained and tested on a database of patients who were registered for cardiac transplantation in the United States between 1985 and 2015. The more it learns, the more accurate it gets.

The team hoped that this approach would allow ToPs to provide personalized risk analyses of individual patients, not a “one-size-fits-all” approach of previous machine learning models that apply to large numbers of prospective recipients.

It seems like it did the trick: ToPs appeared to significantly outperform older algorithms, and clinical practitioner methods, both in terms of survival and mortality predictions pre- and post-transplantation. If applied to the real world, there’s a solid chance that this would save more lives by identifying compatible recipients and donors more accurately and often than conventional methods are able to.

The team also point out that at present, two-thirds of available hearts are actually discarded; ToPs could therefore increase the number of successful transplantations taking place.


This latest piece of tech may have seemed surprising a few years back, but not anymore; we now live in a world where AI programs are able to pick suitable embryos during IVF treatment, and detect malignant cancer tissue, often better than the humans that designed and “taught” the programs in the first place.

There will always be a place for medical practitioners, of course, but these programs – which can’t get tired or make clumsy mistakes – will provide a vital augmentation to these experts as they work. Makes a nice contrast to killer AI stories, don’t you think?


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