NSF Grant Funds Infrastructure to Store Sensitive Health Data

Researchers at the University of Massachusetts Medical School and UMass Lowell are collaborating on a new cyber infrastructure that will enable patients, researchers and physicians to transport and store large quantities of sensitive health information.


Researchers at the University of Massachusetts Medical School and UMass Lowell are collaborating on a new cyber infrastructure that will enable patients, researchers and physicians to transport and store large quantities of sensitive health information.

Under a $1 million National Science Foundation grant, the system— called Flexware—will be piloted in a clinical trial to more accurately estimate the caloric intake of obese patients, who will share photos and videos as well as audio recordings over the secure network.

Estimation of caloric intake is a challenging task and vexing research problem because patients often can't remember what they ate or drank. By providing them with a multimedia “food journal” that improves the process of self-reporting dietary intake and reduces patient bias, researchers believe the system has the potential to better manage weight-related conditions such as diabetes and enable accurate estimation of dietary intake for assessing the effectiveness of weight loss interventions.

“To ensure the security of the information, we have several mechanisms,” says Yu Cao, co-principal investigator and assistant professor of computer science at UMass Lowell. “When we capture the patient data, we actually remove the patient identifiable information including blurring their face when they record food intake. And we encrypt all the data to ensure their privacy is preserved.”

The two-year project includes development of a mobile cloud dietary assessment tool and new machine-learning-based computing techniques, as well as the creation of a large-scale multimedia food database.

“Right now, we’re working on dietary management. But this technology can be extended to other applications such as remote patient monitoring of the elderly at home with chronic diseases such as diabetes,” concludes Cao.

 

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