Innovators Under 35
INNOVATIVE Pioneers
P8. Jenna Wiens, 31
University
of Michigan
Her computational models
identify patients who are most at risk of a deadly infection.
A
sizable percentage of hospital patients end up with an infection they didn’t
have when they arrived.
Among
the most lethal of these is Clostridium difficile. The bacterium,
which spreads easily in hospitals and other health-care facilities, was the
source of almost half a million infections among patients in the United States
in a single year, according to a 2015 report by the Centers for Disease Control
and Prevention. Fifteen thousand deaths were directly attributable to the bug.
Jenna
Wiens, an assistant professor of computer science and engineering at the University
of Michigan, thinks hospitals could learn to prevent many infections and deaths
by taking advantage of the vast amounts of data they already collect about
their patients.
“I
think to really get all of the value we can out of the data we are collecting,
it’s necessary to be taking a machine-learning and a data-mining
approach,”
she says.
she says.
Wiens
has developed computational models that use algorithms to search through the
data contained in a hospital’s electronic health records system, including patients’
medication prescriptions, their lab results, and the records of procedures that
they’ve undergone. The models then tease out the specific risk factors
for C. difficile at that hospital.
“A
traditional approach would start with a small number of variables that we
believe are risk factors and make a model based on those risk factors. Our
approach essentially throws everything in that’s available,” Wiens says. It can
readily be adapted to different types of data.
Aside
from using this information to treat patients earlier or prevent infections
altogether, Wiens says, her model could be used to help researchers carry out
clinical trials for new treatments, like novel antibiotics. Such studies have
been difficult to do in the past for hospital-acquired infections like C.
difficile—the infections come on fast so there’s little time to enroll a
patient in a trial. But by using Wiens’s model, researchers could identify
patients most vulnerable to infections and study the proposed intervention
based on that risk.
At
a time when health-care costs are rising exponentially, it’s hard to imagine
hospitals wanting to spend more money on new machine-learning approaches. But
Wiens is hopeful that hospitals will see the value in hiring data scientists to
do what she’s doing.
“I
think there is a bigger cost to not using the data,” she says. “Patients are
dying when they seek medical care and they acquire one of these infections. If
we can prevent those, the savings are priceless.”
—Emily Mullin
MIT TECHNOLOGY REVIEW
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