As hospitals collect more and more patient data, it can be difficult to know how to use it. Jenna Wiens wants to use computer science to transform data into actionable improvements to patient clinical care.
“The data are there. It’s a matter of leveraging them and asking the right questions,” she said.
Jenna Wiens, assistant professor of electrical engineering and computer science, grew up in Ottawa, Canada. She loved math and pursued engineering after participating in a weeklong course at a local university that introduced her to the field.
It was during her graduate studies at the Massachusetts Institute of Technology that she realized she wanted to enter academia.
“Halfway through grad school I had the opportunity to TA and realized how much I enjoyed teaching and mentoring students. I also loved the academic freedom that came with research. I got to ask the questions,” she said.
She completed a doctorate in computer science at MIT and came to the University of Michigan in 2014 after having the opportunity to interact with the EECS department and research collaborators at Michigan Medicine.
“It become obvious that U-M is the place to be for my work,” she said.
Wiens’ research focuses on harnessing patient data and increasing the utility of machine learning in clinical care, an aim that won her the National Science Foundation’s prestigious CAREER Award in 2016, presented to early career teacher-scholars.
Specifically, she uses machine learning to advance precision health. Machine learning creates algorithms that fine-tune themselves, lending the discipline well to precision health, which utilizes customized, patient-specific medical care. She was attracted to studying medical care for its utility and complexity.
“There are many technical challenges. The data are heterogeneous, time-varying, and high-dimensional, and require interpretable and robust models,” she said.
One application of her work has been in fighting C. difficile infections. C. diff is a potentially deadly, bacterium that takes over the gut microbiome of people whose healthy microbiome is wiped out by antibiotics that they take for other illnesses, allowing the infection to flourish in hospitals. Hospitals struggle to prevent the disease.
Wiens formulated a machine learning algorithm that analyzes tens of thousands of medical variables instead of just the traditional six known risk factors. Her model far outperformed the traditional one, earning her mention in Forbes’ 30 Under 30 in 2015, the MIT Technology Review’s 35 Innovators Under 35 in 2017, and a 5-year, $9.2 million grant from the National Institute of Health in 2016 to continue her research.
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Her recent work has taken her out of the hospital and into homes, namely to Type 1 diabetes patients’ continuous glucose monitors. Glucose monitors constantly collect patient data, which users check to know when to take insulin. Wiens utilized this data to create a machine learning model that caters to each patient’s body and diet, and predicts future blood glucose values with the ultimate goal of estimating the correct level of insulin.
“Instead of predicting a single value in the future, we predict the trajectory. Knowing not just where the patient will be in 60 minutes but also how they will get there is more useful,” she said.
Ultimately, as the volume of health data increases, so will the need to make sense of it. Machine learning is one such method to improve patient-level clinical care.
“Health care is a continuously evolving field. Bacteria and drugs are evolving, too. We’ll need solutions that can similarly adapt and evolve with limited human intervention,” she said.
Q & A
What moment in the classroom stands out as the most memorable?
Anytime a student tells me they’re using what they learned in my class in another course.
What can’t you live without?
Coffee.
Where is your favorite spot on campus?
The peony garden in the Nichols Arboretum in May and June.
What inspires you?
The opportunity to improve health.
What are you currently reading?
The Upside of Stress, by Kelly McGonigal.
Who had the biggest/greatest influence on your career path?
My mother.
