Researchers from KCS are Helping to Drive Innovations in Digital Health Care

CS Prof. Hong Yu in front of Miner School sign
Computer Science Prof. Hong Yu is founding director of the Center of Biomedical and Health Research in Data Sciences (CHORDS).

06/09/2023
By Ed Brennen

Many of Hong Yu’s family members are medical doctors, including her husband and her sister. But Yu, a computer science professor in the Miner School of Computer and Information Sciences, was never interested in becoming a physician.
“To be honest, I can’t be a doctor because I can’t remember all those drug names — they’re alien to me!” she says with a laugh before striking a more serious tone. “And I cannot see blood and people dying. It’s too much for me.”
Instead, Yu has pursued a career in biomedical data science with an expertise in artificial intelligence (AI). In doing so, she has found a way to make important contributions to the medical profession that help people lead healthier lives.
“I have recognized the power of artificial intelligence throughout my career, and I see the impact that AI can make on people’s health,” says Yu, who founded the Center of Biomedical and Health Research in Data Sciences (CHORDS) shortly after joining ĐÓ°É´ŤĂ˝ in 2018. “My goal is to make the health care system better and help make a change in people’s lives.”
The average person is likely to generate more than 1 million gigabytes of health-related dada in their lifetime, enough to fill 300 million books.
Yu is among a growing number of researchers from the Kennedy College of Sciences who are driving innovations in digital health care, a rapidly expanding field that spans everything from mobile health, telehealth and health data to robotics, wearable devices and personalized medicine. Faculty members are, among other things, creating new diagnostic tools, harnessing Big Data to improve patient outcomes and advancing the next generation of personalized AI systems.
And they’re doing so at a time of explosive growth in digital health care. Consider these statistics:
  • Approximately 30% of the world’s data volume is being generated by the health care industry, according to a recent report by one of the world’s largest banks, RBC Capital Markets, with an exponential amount of new data being created daily that’s mined for valuable insights.
  • The average person is likely to generate more than 1 million gigabytes of health-related data in their lifetime, enough to fill 300 million books, according to IBM Research.
  • The U.S. Food and Drug Administration has added 178 artificial intelligence- and machine learning-enabled medical devices to its approved list in the past year, bringing the total number of approved devices to 521.
  • In the U.S., revenue in the digital health market is projected to top $32 billion this year and grow to more than $42 billion by 2027, according to one industry estimate. Another market researcher expects the global digital health market to reach $1.5 trillion by 2030.
digital-health-liu-yuUMass Center for Digital Health Co-Directors Yu Cao, left, and Benyuan Liu developed a tool to help diagnose tuberculosis.
Digital health care has been gaining momentum for years, and innovations such as electronic medical records and wearable gadgets like Fitbits that track vital signs are now commonplace. The COVID-19 pandemic only accelerated this growth by demonstrating technology’s potential to improve access, reduce inefficiencies and speed innovation. In the early days of the pandemic, for instance, contact tracing apps were developed to monitor outbreaks at the local level. Doctor’s appointments, meanwhile, moved from the exam room to the video screen — a shift that appears to have some staying power. According to Deloitte’s “2022 Connectivity and Mobile Trends Survey,” 49% of consumers said they had attended at least one virtual medical appointment in the past year, including 59% of millennials.
“The field of digital health is still really evolving, with new problems, new techniques and new opportunities,” says Computer Science Prof. Benyuan Liu, co-director of the UMass Center for Digital Health. “We are working in a very exciting area with people from different fields, developing technical solutions for real-world problems. And we are educating the next generation of technical people.”
Here’s a closer look at some of the work being done by Kennedy College of Sciences researchers to advance digital health care.

Connecting Care For Older Adults

Ph.D. student Vijeta Deshpande writes on a white board
Computer Science Ph.D. student Vijeta Deshpande has analyzed 30 million tweets.
For older adults living on their own, voice-activated smart home devices like Amazon Alexa and Google Home can be useful tools for turning on lights and setting reminders to take medication. But what if these devices could also alert someone if they left something cooking on their stove? Or detect a gradual change in the user’s speech pattern that indicates a medical concern?
Several faculty members are working on advances like these as part of AI-CARING, or the AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups. Funded in 2021 by a five-year, $20 million National Science Foundation grant (with industry sponsorship from Amazon and Google), AI-CARING brings together more than 40 faculty members from ĐÓ°É´ŤĂ˝, Georgia Tech, Carnegie Mellon University, Oregon State University and Oregon Health & Science University. They are developing the next generation of personalized, collaborative AI systems, including sensors and smart home technologies, that improve the quality of life and independence of “aging in place” adults, particularly those diagnosed with mild cognitive impairment (MCI).
According to the U.S. Census Bureau, 17% of the U.S. population, or 56.1 million people, was 65 years or older in 2020. By 2060, that number is projected to climb to 94.7 million people, or 23% of the country. Approximately 20% of people over 65, meanwhile, have been diagnosed with age-related cognitive decline associated with MCI. The burden for their care often falls on family members, since MCI patients are not eligible for subsidized care facilities.
“We’re an aging population, and there will not be as many people to provide care for every older person,” says Distinguished University Prof. of Computer Science Holly Yanco, co-principal investigator on AI-CARING and chair of the Miner School of Computer and Information Sciences. “Finding ways to offload some of the burden for care that doesn’t really require a personal touch, like scheduling an appointment, is something that AI can do.”
ĐÓ°É´ŤĂ˝, which received a $2.4 million share of the funding, is contributing to the work in several ways.
Computer Science Asst. Prof. Reza Ahmadzadeh, an expert in technologies that allow AI systems to “learn” and interact with people, is researching how to improve the performance and cooperation of an aging adult’s support team, which includes both humans (doctors, family members and neighbors) and technology (voice assistants, sensors and robots).
Plotting each team member, or “agent,” on a graph, Ahmadzadeh rates their connections to one another based on how much information is shared between them (0 for nothing to 1 for everything). Using this data, he is developing algorithms to answer two questions: How will the team perform given its structure, and “more importantly,” he says, what structure is required to achieve a desired performance?
“It’s interesting to me to build a team that not only helps you with one task immediately today, but that also adapts to your changes to help you later,” says Ahmadzadeh, who is working with two computer science students, Ph.D. candidate Saniya Vahedian Movahed and undergraduate Monish Reddy Kotturu. “The main goal, to me, is for people to be able to sustain independence and improve their quality of life. Anything we can do in that direction, I will be happy.”
Yanco, director of the New England Robotics Validation and Experimentation (NERVE) Center at UML, is working with Adam Norton, the NERVE Center’s associate director, on metrics and benchmarking for the interactive AI systems being developed. She is also working with Computer Science Prof. Fred Martin, associate dean for teaching, learning and undergraduate studies, on creating educational programs in AI and robotics for K-12 students to ensure that there’s a skilled and diverse future workforce in the field.
Outside of the Kennedy College, meanwhile, Electrical and Computer Engineering Asst. Prof. Paul Robinette is identifying ethics and trust issues related to the use of voice assistants. 
“We don’t want AI to take the place of somebody having hands-on care from a doctor or nurse. That personal interaction is important,” Yanco says. “AI can assist us with health care, but it doesn’t replace it. It’s a tool, like an X-ray machine or any other diagnostic machine.”

Building Diagnostic Platforms For Community Health

Benyuan Liu was in a remote region of Peru in 2013 when he realized just what kind of an impact he could have on people’s health. The computer science professor was laying the groundwork for a digital health tool that aims to accelerate and improve the diagnosis of tuberculosis (TB), a curable bacterial infection that kills 1.6 million people globally each year, according to the World Health Organization.  
“It was an eye-opening experience for me to see how technology can be used to improve the quality of health care,” says Liu, whose parents were both medical doctors. “It opened the door for me to develop technical solutions to address important health problems in our society.”
It also helped spawn UML’s Center for Digital Health, which Liu co-directs with Computer Science Assoc. Prof. Yu Cao. Started in 2016 with a $125,000 grant from the UMass President’s Science & Technology Initiatives Fund, the center brings together computer scientists, biostatisticians, epidemiologists, clinical practitioners, biomedical researchers and information security specialists from UML, UMass Chan Medical School and UMass Boston. Their goal is to use digital technology such as cloud computing, Big Data analytics, sensor monitoring and mobile devices to improve the quality, efficiency and effectiveness of public health care.
Their recently completed TB diagnostic tool, called “eRx,”was developed with input from U.S. and Peruvian physicians, clinicians and other public health professionals. The web-based system enables nurses and health care workers at remote TB clinics to send a patient’s digitized chest X-rays to a cloud-computing server via a smartphone app. A pulmonary specialist can log in to view the images remotely on a computer or tablet and make an immediate diagnosis.
The team also created a database of over 10,000 X-ray images—“one of the largest and best-annotated” of its kind, Cao says — which it used to develop a machine-learning algorithm that can automatically analyze new X-rays and assist physicians in identifying possible signs of TB. 
“It can be a game-changer for TB diagnosis,” Cao says of the project, which was funded by a four-year, $1.3 million grant from the National Institutes of Health and the National Science Foundation through the interagency program Smart and Connected Health.
The hope, Liu and Cao say, is that the eRx system can be adapted to better diagnose other infectious diseases such as COVID-19. 
The technology is already being applied to another community-based project closer to home. Last fall, a multidisciplinary UML team, led by Civil and Environmental Engineering Prof. Pradeep Kurup, received a four-year, $2.5 million NSF research grant to test and monitor the quality and safety of drinking water for thousands of Merrimack Valley residents. 
“The idea is to develop a social technology platform for ‘citizen scientists’ in the community to monitor the drinking water safety,” explains Liu, who is working on the project with Cao and Computer Science Asst. Prof. Mohammad Arif Ul Alam. “Using a smartphone app, they can upload the results to the cloud. We will develop a machine-learning algorithm to help identify where the source of contaminants is coming from in the water pipe systems.”
For Cao, projects such as these are an exciting opportunity to apply his theoretical research.
“When you’re contributing to lifesaving tools and infrastructures for health care, you feel you’re making a real impact,” he says. “It’s not just your paper contribution, but also really impacting human life.”

Turning Tweets Into Public Health Tools

Following his controversial $44 billion acquisition of Twitter, Elon Musk tweeted that he bought the social media platform to “help humanity.”
Researchers from the Kennedy College are a step ahead of him. Second-year computer science Ph.D. student Vijeta Deshpande has been working with Prof. Hong Yu on a project that uses natural language processing and machine learning to analyze Twitter data and create an algorithm that can predict adverse health outcomes at the community level. Their tool could eventually be used by public health officials to direct resources and plan interventions in advance of a crisis.
“I love this project,” says Yu, who hopes that it receives NIH funding to match the support it has already garnered from Chancellor Julie Chen and U.S. Rep. Lori Trahan.
The work stems from a project that Yu and her students took on during the early days of the COVID-19 pandemic, when they noticed more people experiencing mental health issues and food insecurity.
“We wanted to use AI to help,” says Yu, whose team began by mapping the geolocations of the nearly 40,000 food pantries across the U.S. Using data from the 2010 U.S. Census, they then analyzed the socioeconomic status of more than 200,000 “block groups” across the country, a subdivision of census data that zooms into population clusters as small as 600 people and provides a more “homogenous” view than city or county data provides.
“In New York City, for instance, two different blocks can have a huge income disparity,” Yu says.
Their soon-to-be-published findings show that in some rural and urban communities, “it’s the rich neighborhoods that have access to food pantries, while the poor neighborhoods have much less access,” Yu says. “There is a great deal of disparity.”
Knowing that census figures can be quickly outdated, however, Yu’s team then turned to Twitter (and its millions of active U.S. users) for better real-time data on mental health and food insecurity.
“There is a lot of diversity in the population that uses Twitter, so we have a good representation. And the data accessibility is great,” says Deshpande, who notes that Twitter allows academic bodies to download 10 million tweets per month for research purposes.
So far, Deshpande has analyzed 30 million tweets from 1,000 block groups across the country. He began by targeting tweets with the keywords “mental health” and “food insecurity,” then showed a positive correlation to the existing survey data on those topics in the corresponding block groups. He took the model a step further by augmenting it for tweets that mention social determinant factors of health, such as housing insecurity, which only improved the results.
“We were quite excited to see that we are able to get better results with the social determinant data,” says Deshpande, who went on to examine the full text content of the tweets using neural networks and natural language processing, an approach that Yu says reproduces survey data at nearly 80% accuracy.
“If we can train the neural network for future years, we can detach ourselves from conducting surveys and make quick predictions about health outcomes,” Deshpande says. 
As co-director of CHORDS, Yu has worked on AI projects that help detect physician errors and identify people at risk of suicide. While she could never be a medical doctor, she is proud of the impact she is making on people’s lives.
“This field is full of golden opportunities,” she says. “We can save lots of lives because of AI. It is possible we could have a way to cure cancer and other diseases. All these major advances in medicine, to a large extent, are because of AI.” 

Harnessing Big Data To Fight Cancer

In her Computational Cancer Biology Lab, Asst. Prof. of Biology Rachel Melamed connects health data to molecular data to try to understand what causes not only cancer, but also other diseases such as Alzheimer’s and diabetes.
“All we do is look at data,” says Melamed, who mines datasets from health records, biobanks, cancer genomics projects and experimental drug studies to investigate how a disease’s development might be influenced by other health conditions and drug combinations.
“Once you have all that information on people, you can try to figure out all these complex causes of disease, like how genetics might interact with something that happens during your life to impact your risk of having cancer,” she says. “Now, we don’t have to look at one factor at a time; we can try to look at the combinations of factors.”
In a recent study of drug combinations, for instance, Melamed and her team found that a combination of fish oil and fenofibrate, a medication used to treat abnormal blood lipid levels, could affect a person’s odds of getting cancer.
“They’re not the most common drugs, and they would have never been tested together before,” she says. “So, the only way to discover something like that is by looking at huge datasets and seeing the association between people who take these drugs and whether or not they get cancer down the line.”
Melamed’s path to becoming a computational biologist — someone who uses data analysis, mathematical modeling and computational simulations to understand biological systems and relationships — started with a bachelor’s degree in computer science from Brown University. A brief stint as a software engineer proved unfulfilling, however, so she joined an immunology lab as a data analyst. 
“A lot of people who get a computer science degree go on to do software engineering and work at companies like Facebook or Google, and I was just never really interested in that,” says Melamed, who wanted to make a more “positive societal impact with my work” through public health. 
She began to notice “more and more biology data being generated” and realized there was a demand for people who had the computational background to work with that data. So, she got a Ph.D. in biomedical informatics from Columbia University, where she worked on data from the Cancer Genome Atlas, a landmark program that analyzed thousands of samples from 33 types of cancer.
“It used to be that one person, maybe an M.D. at a big medical center, would gather data from their patients, and only they could use it. But now there’s a big effort to make as much data as possible and let everyone use it, which creates so much more possibility,” she says.
After a postdoc in biomedical data science at the University of Chicago, Melamed joined the Kennedy College in 2020. She teaches courses in cancer genomics and data science.
“When a lot of people think of biology, they’re like, ‘OK, what can I do with that? I could be a biology teacher or a doctor.’ And they don’t know about all this other stuff,” she says. “But there are so many companies in the Boston area doing this work and looking for computational biologists. It’s a good field for an undergraduate to pursue.

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