Google is training computers to predict when you might get sick
Google is training computers to predict when you might
get sick
Google Brain is working with top hospitals to predict
health outcomes from medical data.
That data was stripped of personally-identifiable
information before it was shared with Google.
This is the latest in a series of research projects from
Google to apply its machine learning expertise to health care.
Christina Farr May 17, 2017
Google is building tools to predict when you'll get sick.
The company is applying its machine learning expertise,
which it originally developed for consumer products like Translate and Image
Search, to health care. To get there, it worked with hospitals, including
Stanford Medicine, UC San Francisco and The University of Chicago Medicine,
which stripped millions of patient medical records of personally identifying
data and shared them with Google's research team, Google Brain.
"We can improve predictions for medical events that
might happen to you," said Katherine Chou, the head of product at Google
Brain, in an interview with CNBC. "We have validated the data and seen
promising results." Those results will not be released until a formal
review process.
Hospitals are increasingly under the gun to keep patients
healthy and out of the emergency room. Increasingly, health systems are
shifting away from "fee for service" models, in which they get paid
for pricey tests and procedures, to "value-based care," where they're
rewarded for improving health outcomes. That shift is a big opportunity for Silicon
Valley's technology companies and startups, which are working with existing
data to help hospitals take proactive steps to keep their patients healthy.
So, for instance, a computer might soon determine the
likelihood that certain patients will acquire a potentially life-threatening
disease like sepsis, or end up being readmitted after being discharged from the
hospital.
The advance also addresses a big problem in medical
specialties like radiology and pathology, where clinicians are saddled with a
massive amount of information and too little time. Even a well-trained human
eye can occasionally miss something.
Many of the top hospitals have their own technology
teams, but they pale in comparison to the computing talent at Google. For Atul
Butte, director of the Institute of Computational Health Sciences at UCSF, the
draw was Google's amazing in-house machine learning expertise.
Butte said the project "bubbled up" because
UCSF has a wealth of medical data, including admissions reports, medical
records, diagnoses, lab results and so on, but it has not yet mined this
information to make predictions about patient outcomes.
"This isn't a research project," he said.
"It's more of a scientific collaboration around improving the quality of
care for patients."
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