Google Is Using Machine Learning to Predict When a Patient Will Die
Google Is Training Machines to Predict When a Patient
Will Die
By Mark Bergen 18 June 2018, 8:40 AM 18 June 2018, 2:00
AM
(Bloomberg) -- A woman with late-stage breast cancer came
to a city hospital, fluids already flooding her lungs. She saw two doctors and
got a radiology scan. The hospital’s computers read her vital signs and
estimated a 9.3 percent chance she would die during her stay.
Then came Google’s turn. An new type of algorithm created
by the company read up on the woman -- 175,639 data points -- and rendered its
assessment of her death risk: 19.9 percent. She passed away in a matter of
days.
The harrowing account of the unidentified woman’s death
was published by Google in May in research highlighting the health-care
potential of neural networks, a form of artificial intelligence software that’s
particularly good at using data to automatically learn and improve. Google had
created a tool that could forecast a host of patient outcomes, including how
long people may stay in hospitals, their odds of re-admission and chances they
will soon die.
What impressed medical experts most was Google’s ability
to sift through data previously out of reach: notes buried in PDFs or scribbled
on old charts. The neural net gobbled up all this unruly information then spat
out predictions. And it did it far faster and more accurately than existing
techniques. Google’s system even showed which records led it to conclusions.
Hospitals, doctors and other health-care providers have
been trying for years to better use stockpiles of electronic health records and
other patient data. More information shared and highlighted at the right time
could save lives -- and at the very least help medical workers spend less time
on paperwork and more time on patient care. But current methods of mining
health data are costly, cumbersome and time consuming.
As much as 80 percent of the time spent on today’s
predictive models goes to the “scut work” of making the data presentable, said
Nigam Shah, an associate professor at Stanford University, who co-authored
Google’s research paper, published in the journal Nature. Google’s approach
avoids this. "You can throw in the kitchen sink and not have to worry
about it,” Shah said.
Google’s next step is moving this predictive system into
clinics, AI chief Jeff Dean told Bloomberg News in May. Dean’s health research
unit -- sometimes referred to as Medical Brain -- is working on a slew of AI
tools that can predict symptoms and disease with a level of accuracy that is
being met with hope as well as alarm.
Inside the company, there’s a lot of excitement about the
initiative. "They’ve finally found a new application for AI that has
commercial promise," one Googler says. Since Alphabet Inc.’s Google
declared itself an “AI-first” company in 2016, much of its work in this area
has gone to improve existing internet services. The advances coming from the
Medical Brain team give Google the chance to break into a brand new market --
something co-founders Larry Page and Sergey Brin have tried over and over
again.
Software in health care is largely coded by hand these
days. In contrast, Google’s approach, where machines learn to parse data on
their own, “can just leapfrog everything else,” said Vik Bajaj, a former
executive at Verily, an Alphabet health-care arm, and managing director of
investment firm Foresite Capital. “They understand what problems are worth
solving," he said. "They’ve now done enough small experiments to know
exactly what the fruitful directions are.”
Dean envisions the AI system steering doctors toward
certain medications and diagnoses. Another Google researcher said existing
models miss obvious medical events, including whether a patient had prior
surgery. The person described existing hand-coded models as “an obvious,
gigantic roadblock” in health care. The person asked not to be identified
discussing work in progress.
For all the optimism over Google’s potential, harnessing
AI to improve health-care outcomes remains a huge challenge. Other companies,
notably IBM’s Watson unit, have tried to apply AI to medicine but have had
limited success saving money and integrating the technology into reimbursement
systems.
Google has long sought access to digital medical records,
also with mixed results. For its recent research, the internet giant cut deals
with the University of California, San Francisco, and the University of Chicago
for 46 billion pieces of anonymous patient data. Google’s AI system created
predictive models for each hospital, not one that parses data across the two, a
harder problem. A solution for all hospitals would be even more challenging.
Google is working to secure new partners for access to more records.
A deeper dive into health would only add to the vast
amounts of information Google already has on us. "Companies like Google
and other tech giants are going to have a unique, almost monopolistic, ability
to capitalize on all the data we generate," said Andrew Burt, chief
privacy officer for data company Immuta. He and pediatric oncologist Samuel
Volchenboum wrote a recent column arguing governments should prevent this data
from becoming "the province of only a few companies," like in online
advertising where Google reigns.
Google is treading carefully when it comes to patient
information, particularly as public scrutiny over data-collection rises. Last
year, British regulators slapped DeepMind, another Alphabet AI lab, for testing
an app that analyzed public medical records without telling patients that their
information would be used like this. With the latest study, Google and its
hospital partners insist their data is anonymous, secure and used with patient
permission. Volchenboum said the company may have a more difficult time maintaining
that data rigor if it expands to smaller hospitals and health-care networks.
Still, Volchenboum believes these algorithms could save
lives and money. He hopes health records will be mixed with a sea of other
stats. Eventually, AI models could include information on local weather and
traffic -- other factors that influence patient outcomes. "It’s almost
like the hospital is an organism," he said.
Few companies are better poised to analyze this organism
than Google. The company and its Alphabet cousin, Verily, are developing
devices to track far more biological signals. Even if consumers don’t take up
wearable health trackers en masse, Google has plenty of other data wells to
tap. It knows the weather and traffic. Google’s Android phones track things
like how people walk, valuable information for measuring mental decline and
some other ailments. All that could be thrown into the medical algorithmic
soup.
Medical records are just part of Google’s AI health-care
plans. Its Medical Brain has unfurled AI systems for radiology, ophthalmology
and cardiology. They’re flirting with dermatology, too. Staff created an app
for spotting malignant skin lesions; a product manager walks around the office
with 15 fake tattoos on her arms to test it.
Dean, the AI boss, stresses this experimentation relies
on serious medical counsel, not just curious software coders. Google is
starting a new trial in India that uses its AI software to screen images of
eyes for early signs of a condition called diabetic retinopathy. Before
releasing it, Google had three retinal specialists furiously debate the early
research results, Dean said.
Over time, Google could license these systems to clinics,
or sell them through the company’s cloud-computing division as a sort of
diagnostics-as-a-service. Microsoft Corp., a top cloud rival, is also working
on predictive AI services. To commercialize an offering, Google would first
need to get its hands on more records, which tend to vary widely across health
providers. Google could buy them, but that may not sit as well with regulators
or consumers. The deals with UCSF and the University of Chicago aren’t
commercial.
For now, the company says it’s too early to settle on a
business model. At Google’s annual developer conference in May, Lily Peng, a
member of Medical Brain, walked through the team’s research outmatching humans
in spotting heart disease risk. "Again," she said. "I want to
emphasize that this is really early on."
©2018 Bloomberg L.P.
Comments
Post a Comment