Nav: Home

Artificial intelligence can predict premature death, study finds

March 27, 2019

Computers which are capable of teaching themselves to predict premature death could greatly improve preventative healthcare in the future, suggests a new study by experts at the University of Nottingham.

The team of healthcare data scientists and doctors have developed and tested a system of computer-based 'machine learning' algorithms to predict the risk of early death due to chronic disease in a large middle-aged population.

They found this AI system was very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts. The study is published by PLOS ONE in a special collections edition of "Machine Learning in Health and Biomedicine".

The team used health data from just over half a million people aged between 40 and 69 recruited to the UK Biobank between 2006 and 2010 and followed up until 2016.

Leading the work, Assistant Professor of Epidemiology and Data Science, Dr Stephen Weng, said: "Preventative healthcare is a growing priority in the fight against serious diseases so we have been working for a number of years to improve the accuracy of computerised health risk assessment in the general population. Most applications focus on a single disease area but predicting death due to several different disease outcomes is highly complex, especially given environmental and individual factors that may affect them.

"We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person's risk of premature death by machine-learning. This uses computers to build new risk prediction models that take into account a wide range of demographic, biometric, clinical and lifestyle factors for each individual assessed, even their dietary consumption of fruit, vegetables and meat per day.

"We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and 'hospital episodes' statistics. We found machine learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."

The AI machine learning models used in the new study are known as 'random forest' and 'deep learning'. These were pitched against the traditionally-used 'Cox regression' prediction model based on age and gender - found to be the least accurate at predicting mortality - and also a multivariate Cox model which worked better but tended to over-predict risk.

Professor Joe Kai, one of the clinical academics working on the project, said: "There is currently intense interest in the potential to use 'AI' or 'machine-learning' to better predict health outcomes. In some situations we may find it helps, in others it may not. In this particular case, we have shown that with careful tuning, these algorithms can usefully improve prediction.

"These techniques can be new to many in health research, and difficult to follow. We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care."

This new study builds on previous work by the Nottingham team which showed that four different AI algorithms, 'random forest', 'logistic regression', 'gradient boosting' and 'neural networks', were significantly better at predicting cardiovascular disease than an established algorithm used in current cardiology guidelines. This earlier study is available here.

The Nottingham researchers predict that AI will play a vital part in the development of future tools capable of delivering personalised medicine, tailoring risk management to individual patients. Further research requires verifying and validating these AI algorithms in other population groups and exploring ways to implement these systems into routine healthcare.
-end-


University of Nottingham

Related Health Articles:

Mental health of health care workers in china in hospitals with patients with COVID-19
This survey study of almost 1,300 health care workers in China at 34 hospitals equipped with fever clinics or wards for patients with COVID-19 reports on their mental health outcomes, including symptoms of depression, anxiety, insomnia and distress.
Health records pin broad set of health risks on genetic premutation
Researchers from the University of Wisconsin-Madison and Marshfield Clinic have found that there may be a much broader health risk to carriers of the FMR1 premutation, with potentially dozens of clinical conditions that can be ascribed directly to carrying it.
Attitudes about health affect how older adults engage with negative health news
To get older adults to pay attention to important health information, preface it with the good news about their health.
Geographic and health system correlates of interprofessional oral health practice
In the current issue of Family Medicine and Community Health (Volume 6, Number 2, 2018, pp.
Bloomberg era's emphasis on 'health in all policies' improved New Yorkers' heart health
From 2002 to 2013, New York City implemented a series of policies prioritizing the public's health in areas beyond traditional healthcare policies and illustrated the potential to reduce cardiovascular disease risk.
Youth consider mobile health units a safe place for sexual health services
Mobile health units bring important medical services to communities across the country.
Toddler formulas and milks -- not recommended by health experts -- mislead with health claims
Misleading labeling on formulas and milks marketed as 'toddler drinks' may confuse parents about their healthfulness or necessity, finds a new study by researchers at the NYU College of Global Public Health and the Rudd Center for Food Policy & Obesity at the University of Connecticut.
Women's health has worsened while men's health has improved, trends since 1990 show
Swedish researchers have studied health trends among women and men aged 25-34 from 1990-2014.
Health insurance changes, access to care by patients' mental health status
A research letter published by JAMA Psychiatry examined access to care before the Patient Protection and Affordable Care Act (ACA) and after the ACA for patients grouped by mental health status using a scale to assess mental illness in epidemiologic studies.
Community health workers lead to better health, lower costs for Medicaid patients
As politicians struggle to solve the nation's healthcare problems, a new study finds a way to improve health and lower costs among Medicaid and uninsured patients.
More Health News and Health Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Teaching For Better Humans 2.0
More than test scores or good grades–what do kids need for the future? This hour, TED speakers explore how to help children grow into better humans, both during and after this time of crisis. Guests include educators Richard Culatta and Liz Kleinrock, psychologist Thomas Curran, and writer Jacqueline Woodson.
Now Playing: Science for the People

#556 The Power of Friendship
It's 2020 and times are tough. Maybe some of us are learning about social distancing the hard way. Maybe we just are all a little anxious. No matter what, we could probably use a friend. But what is a friend, exactly? And why do we need them so much? This week host Bethany Brookshire speaks with Lydia Denworth, author of the new book "Friendship: The Evolution, Biology, and Extraordinary Power of Life's Fundamental Bond". This episode is hosted by Bethany Brookshire, science writer from Science News.
Now Playing: Radiolab

Space
One of the most consistent questions we get at the show is from parents who want to know which episodes are kid-friendly and which aren't. So today, we're releasing a separate feed, Radiolab for Kids. To kick it off, we're rerunning an all-time favorite episode: Space. In the 60's, space exploration was an American obsession. This hour, we chart the path from romance to increasing cynicism. We begin with Ann Druyan, widow of Carl Sagan, with a story about the Voyager expedition, true love, and a golden record that travels through space. And astrophysicist Neil de Grasse Tyson explains the Coepernican Principle, and just how insignificant we are. Support Radiolab today at Radiolab.org/donate.