AI can predict when someone will die with unsettling accuracy
AI can predict when someone will die with
unsettling accuracy
Do AI systems have a role to play in healthcare?
By Mindy Weisberger, Live Science March 27, 2019, 12:13 PM
PDT
Medical researchers have unlocked an
unsettling ability in artificial intelligence (AI): predicting a person's early
death.
Scientists recently trained an AI
system to evaluate a decade of general health data submitted by more than half
a million people in the United Kingdom. Then, they tasked the AI with
predicting if individuals were at risk of dying prematurely — in other words,
sooner than the average life expectancy — from chronic disease, they reported
in a new study.
The predictions of early death
that were made by AI algorithms were "significantly more accurate"
than predictions delivered by a model that did not use machine learning, lead
study author Dr. Stephen Weng, an assistant professor of epidemiology and data
science at the University of Nottingham (UN) in the U.K., said
in a statement.
To evaluate the likelihood of
subjects' premature mortality, the researchers tested two types of AI:
"deep learning," in which layered information-processing networks
help a computer to learn from examples; and "random forest," a
simpler type of AI that combines multiple, tree-like models to consider
possible outcomes.
Then, they compared the AI models'
conclusions to results from a standard algorithm, known as the Cox model.
DIFFERENT VARIABLES
All three models determined that factors such as age,
gender, smoking history and a prior cancer diagnosis were top variables for
assessing the likelihood of a person's early death. But the models diverged
over other key factors, the researchers found.
The Cox model leaned heavily on ethnicity and physical
activity, while the machine-learning models did not. By comparison, the random
forest model placed greater emphasis on body fat percentage, waist
circumference, the amount of fruit and vegetables that people ate, and skin
tone, according to the study. For the deep-learning model, top factors included
exposure to job-related hazards and air pollution, alcohol intake and the use
of certain medications.
When all the number crunching was done, the deep-learning
algorithm delivered the most accurate predictions, correctly identifying 76
percent of subjects who died during the study period. By comparison, the random
forest model correctly predicted about 64 percent of premature deaths, while
the Cox model identified only about 44 percent.
This isn't the first time that experts have harnessed
AI's predictive power for health care. In 2017, a different team of researchers
demonstrated that AI could learn to spot early signs of Alzheimer's disease;
their algorithm evaluated brain scans to predict if a person would be likely to
develop Alzheimers, and it did so with about 84 percent accuracy, Live Science
previously reported.
Another study found that AI could predict the onset of
autism in 6-month-old babies that were at a high risk of developing the
disorder. Yet another study could detect signs of encroaching diabetes through
analysis of retina scans; and one more — also using data derived from retinal
scans — predicted the likelihood of a patient experiencing a heart attack or
stroke.
In the new study, the scientists demonstrated that machine
learning — "with careful tuning" — can be used to successfully
predict mortality outcomes over time, study co-author Joe Kai, a UN professor
of primary care, said in the statement.
While using AI this way may be unfamiliar to many health
care professionals, presenting the methods used in the study "could help
with scientific verification and future development of this exciting
field," Kai said.
The findings were published online today (March 27) in
the journal PLOS ONE.
Originally published on Live Science.
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