Global Health

This AI could spot early signs of heart failure

A nurse walks with "Pepper" (L) and "Zora" the robots, humanoid robots designed to welcome and take care of visitors and patients, at AZ Damiaan hospital in Ostend, Belgium June 16, 2016. REUTERS/Francois Lenoir

An algorithm can anticipate early signs of heart failure, leading to better preventative care for patients at risk. Image: REUTERS/Francois Lenoir

Kristen Perez
Share:
Our Impact
What's the World Economic Forum doing to accelerate action on Global Health?
The Big Picture
Explore and monitor how Global Health is affecting economies, industries and global issues
A hand holding a looking glass by a lake
Crowdsource Innovation
Get involved with our crowdsourced digital platform to deliver impact at scale
Stay up to date:

Global Health

A new method that uses deep learning to analyze vast amounts of personal health record data could identify early signs of heart failure, researchers say.

A paper, which appears in the Journal of the American Medical Informatics Association (JAMIA), describes how the method addresses temporality in the data—something previously ignored by conventional machine learning models in health care applications.

The research uses a deep learning model to allow earlier detection of the incidents and stages that often lead to heart failure within 6-18 months. To achieve this, researchers use a recurrent neural network (RNN) to model temporal relations among events in electronic health records.

Temporal relationships communicate the ordering of events or states in time. This type of relation is traditionally used in natural language processing. However, researchers saw a new opportunity to leverage the power of RNNs.

“I studied deep learning and I was wondering if RNNs could be introduced into health care. It is a very popular model for processing sequences and is traditionally used for translation,” says Edward Choi, a PhD student at Georgia Tech, working with Jimeng Sun, an associate professor at the School of Computational Science and Engineering.

By utilizing RNN, the algorithm can anticipate early stages of heart failure, which will ultimately lead to better preventative care for patients at risk of heart disease.

“Machine learning is being used in every aspect of health care. From diagnosis and treatments to recommendations for patient care after surgeries. This particular model is focused on deep learning, which has had great success in many industries. However, in health care, we are on the front of pioneering deep learning and Edward is one of the first ones to apply it,” Sun says.

Have you read?

According to the Centers for Disease Control and Prevention, heart failure affects 5.7 million adults in the United States, and half of those who develop heart failure die within 5 years of diagnosis costing the nation an estimated $30.7 billion each year.

The new findings could provide relief to millions of Americans each year by allowing doctors to offer patients early intervention.

“This is a preliminary work, it showed potential that it can do better than classical models—it makes a good promise for how deep learning can make a positive impact in the health care industry,” says Choi.

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

Sign up for free

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:
Global HealthArtificial IntelligenceEmerging Technologies
Share:
World Economic Forum logo
Global Agenda

The Agenda Weekly

A weekly update of the most important issues driving the global agenda

Subscribe today

You can unsubscribe at any time using the link in our emails. For more details, review our privacy policy.

Why we need to strengthen health responses in humanitarian crises

Hans Kluge

February 26, 2024

About Us

Events

Media

Partners & Members

  • Join Us

Language Editions

Privacy Policy & Terms of Service

© 2024 World Economic Forum