Increased adoption of electronic health records is a good thing – but what to do with all that data to enable meaningful clinical data support? That’s what drives Benjamin Marlin, Ph.D., assistant professor at University of Massachusetts Amherst.
“Electronic health records are seeing wide adoption across the United States and we’re starting to see the emergence of large stores of complex clinical data as a result,” says Marlin.
Marlin recently received a five-year, $536,527 National Science Foundation (NSF) Faculty Early Career Development (CAREER) award to develop machine learning-based tools for analyzing complex, large-scale clinical and mobile health (mHealth) data, according to the university.
“There’s significant interest in leveraging these data to enhance all kinds of clinical decision support tools with the hope that they can ultimately improve quality of care,” says Marlin, who is also co-founder and co-director of the UMass Amherst Machine Learning for Data Science Laboratory.
According to a university-issued press release, Marlin’s research will delve into ways to analyze data from emerging mHealth wearable sensor systems that collect large volumes of continuous physiological measurements like respiration and electrocardiogram signals. “Developing models and algorithms that can accurately and reliably detect activities like smoking from wearable sensor data has tremendous potential for use in behavioral science research as well as continuous health monitoring,” says Marlin, a computer scientist by training.
Marlin will work closely with computer scientists, clinicians and medical researchers at UMass Amherst, the University of Memphis, Yale University School of Medicine and Children’s Hospital Los Angeles – all of which will provide access to unique mHealth and clinical data.
“We’re not dealing with nice, clean data in these areas,” Marlin says. “The data are noisy, parts are missing due to sensors disconnecting or clinicians not recording measurements. A number of these issues can break current data analysis methods. The goal of this work is to design new machine learning-based data analysis tools that are significantly more robust and accurate.”
According to NSF, its CAREER award supports junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.
Marlin is also developing a new applied machine learning graduate-level course for the university’s school of computer science.
Aine (“ONya”) Cryts is an on-staff contributing writer for MedTech Boston. She's a political scientist by education, a writer and marketer by trade. She has written for various healthcare technology publications and also served as marketing director at several healthcare software companies in the Boston area. Cryts is an avid volunteer, pet lover and long-distance runner. Story ideas are always welcome.
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