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Federated Learning from Electronic Health Records Paper Receives Best Paper Honors
By Ioannis Paschalidis
Our paper on
``Federated learning of predictive models from federated Electronic Health Records'' (with Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, and Wei Shi), International Journal of Medical Informatics, Vol. 112, April, 2018, pages 59- 67,
has been selected as a Best Paper and is included in the 2019 Yearbook of the
International Medical Informatics Associations (IMIA), Section on Clinical
Research Informatics.
Know What's Good for Your Health? Artificial Intelligence
Big Data, Big Impact
Part Four of a Five-Part Series
Part Five →
Art Jahnke
Illustration by
Carlos Soler
Data and algorithms can spot medical concerns early and point to solutions
A generation ago, the internet changed everything. Today, data science is proving just as revolutionary. Fueled by the abundance of personal information on the internet—yours, ours, everyone's—data science is making business smarter, healthcare more efficient, technology easier, and sports more fun to watch (and play). But it's also made all of us more vulnerable. This article, the fourth in a five-story series, comes as Boston University is investing aggressively into the world of big data, and is poised to build a 17-story Data Sciences Center on Commonwealth Avenue that will house its mathematics and statistics and computer science departments. As BU President Robert A. Brown said: "This is the science that's going to change the way we behave, driving our behavior for the next 50 or 100 years."
Every day, it becomes a little harder to find a corner of healthcare not being touched in some fundamental way by data analytics. That Fitbit on your wrist may soon send your resting heart rate to Google, where it would join the electronic health records of millions of others, and where algorithms could yield comprehensive patient profiles that will be available to healthcare providers anytime, anywhere. Those providers, incidentally, will very likely deliver that care more efficiently after their workflows have been reconfigured by algorithms. Just about everything, from our heartbeats to our blood cell counts to our waiting time at the doctor's office, may soon be tracked, analyzed, compared, and ideally, improved by the mathematical insights of data science. Artificial Intelligence, or AI, is coming to healthcare, and it's coming fast, even as the risks of all that data in the hands of healthcare providers are still being uncovered and explored.
Researchers the world over see big data as a way to help huge communities of people—and individuals. At Boston University, on the macro side, algorithms analyzing millions of geotagged tweets are being used to identify physical characteristics of neighborhoods that contribute to healthier lifestyles. And on the micro side? Algorithms are searching scans of human brains for proteins that may be indicators of Alzheimer's disease.
Let's take a look at a few of the many BU projects that use big data to tackle medical problems by utilizing the multidisciplinary expertise of the University—matching public health experts with computer science people, or electrical engineers with medical doctors.
At the BU School of Medicine, Gerald Denis, a MED associate professor, is building a secure database that will allow him to compare medical information from cancer and diabetes patients at the School of Medicine and in hospitals in Mumbai and Bangalore. Photo by Narinder Nanu/AFP/Getty Images
Answers from the Other Side of the Planet
Gerald Denis, a BU School of Medicine associate professor of pharmacology and medicine, believes an important key to understanding health problems in Boston can be found on the other side of the planet. Denis is building a secure, privacy-preserving database of clinical, molecular, and cellular information from cohorts of cancer and diabetes patients at the School of Medicine and from hospitals in Mumbai and Bangalore.
"This project is meeting a need to compare data across institutions," says Denis, who is working with Anand Devaiah, a MED associate professor of otolaryngology, neurological surgery, and ophthalmology and director of the Biomedical and Health Technology Development & Transfer Domain for BU's Institute for Health System Innovation & Policy. "Here in Boston, for example, we treat a lot of breast cancer patients who are obese and have metabolic complications such as diabetes and inflammation that may exacerbate their risk for cancer metastasis. In India, clinicians also treat patients with breast cancer who are diabetic and inflamed, but who are lean rather than obese. Normally these two populations would never be compared, so we will use new computational tools to pool these two populations to reveal what is shared and what's different."
To preserve the privacy of patients' medical records, Denis turned to Mayank Varia, codirector of the Center for Reliable Information Systems & Cyber Security, who used a cryptography technology called secure multiparty computation, which allows collaborative data analysis without revealing private data. Software for the research was developed by the Software & Application Innovation Lab (SAIL), directed by Andrei Lapets at BU's Rafik B. Hariri Institute for Computing and Computational Science & Engineering. The project, in its pilot phase, was funded by the Dahod Breast Cancer Research Program and the Boston University Digital Health Initiative, a collaborative effort between the Institute for Health System Innovation & Policy and the Hariri Institute for Computing and Computational Science & Engineering.
"Working with the first pieces of data, we've been successful," says Denis. "Next we need to test it with data from Boston and from India. After that, we'll seek funding to build it out."
Data scientist Yannis Paschalidis, an ENG professor (above), is working with BMC physician William G. Adams, a MED professor of pediatrics, on an algorithm that can analyze medical records and spot an increased risk for medical emergencies. Photo by Cydney Scott
Saving Money with Better Predictions
At the School of Medicine, senior data scientist Jerry Sobieraj is helping Boston Medical Center (BMC) bring down the number of patients who return to the hospital within 30 days of treatment, an event that can incur a penalty from Medicaid payers if the return rate exceeds area norms.
Working with algorithms developed by his team of analysts and with data from BMC's electronic medical records, Sobieraj's group categorized patients into three groups: those with a high risk, moderate risk, and low risk of reappearing at the hospital in 30 days. And because BMC has a fair share of patients who are homeless and otherwise hard to keep track of, Sobieraj has further identified a subgroup of high-risk patients who appeared to be "amenable to intervention." Defining that cohort, whose follow-up care can be carefully monitored, may sound uncomplicated, but doing it effectively required the consideration of nearly 200 variables, including diagnoses, medications, clinical observations, fall risk, and red and white blood cell counts.
Sobieraj's group has also built a "low acuity care" model, one that identifies patients who are likely to show up at an emergency room with a medical problem that doesn't need the expensive care provided by emergency departments. That cohort, he says, is responsible for 30 to 35 percent of BMC's 10,000 emergency department visits per month, each of which costs about $300.
Next steps, says Sobieraj, will involve devising an effective means of contacting that group of patients and educating them about the costs and inconvenience of emergency care overuse.
Like unnecessary emergency room visits, hospitalizations that could be avoided have been the elephant in the hospital room for decades. The latter is the target of research conducted by data scientist Yannis Paschalidis, a College of Engineering professor of electrical and computer engineering, and research partner William G. Adams, a BMC physician and a MED professor of pediatrics. Paschalidis was spurred to action several years ago, when he learned that in 2006, the United States spent about $30.8 billion on hospitalizations that could have been prevented through better patient care, healthier patient behavior, or improved ambulatory services. Now, with help from a team of graduate students and nearly $2 million from the National Science Foundation, he and Adams have built an algorithmic model whose analysis of medical records can flag patients at increased risk for medical emergencies with greater than 80 percent accuracy. The two researchers have so far focused on chronic heart disease and diabetes, and they aimed at patients who would require hospitalization within a year. "In terms of cost," says Paschalidis, "care for these patients is a large percent of preventable hospital care."
The researchers are also at work on a similar project, aimed at reducing the number of readmission penalties, using algorithmic analysis of electronic health records from general surgery patients, to predict which patients are likely to reappear at BMC within 30 days of their procedure.
Yet another BU project has teamed up Paschalidis with systems engineering division head Christos Cassandras, an ENG professor of electrical and computer engineering and of systems engineering, and Rebecca Mishuris(MED'08), a MED assistant professor of medicine. Fueled by a three-year, $900,000 grant from the National Science Foundation, the researchers are developing a pilot health informatics system to identify patients who are at risk of heart disease or diabetes, which alone costs the United States about $5.8 billion for hospitalizations that could be prevented.
Spotting those patients sooner would enable early intervention, and ideally, says Paschalidis, personalized treatment of those patients, based in part on outcomes of treatments with different drugs.
Like the Google Fitbit plan, the project will use algorithms that incorporate data from electronic health records and from real-time sources, including wearable, implantable, and home-based networked diagnostic devices.
"Eventually, we hope to move from predictions to prescriptions," says Paschalidis. "We have some initial results for diabetes and hypertension. The goal is to make recommendations available in electronic health records as guidance for the care provider."
Vijaya Kolachalama's lab developed an algorithm to quickly identify signs of illness in kidneys, shown in the red boxes in the image above. Current methods of identification are manual, labor-intensive, and nonstandardized.
Better Scans, Faster Answers
Elsewhere on the Medical Campus, Vijaya Kolachalama, a MED assistant professor of medicine, has written algorithms that may spot evidence of Alzheimer's disease in scans of an entire brain, a big step forward from the single brain section scans of earlier quests for Alzheimer's. The more comprehensive image not only offers researchers vastly more information, it gives it to them much faster. "Before this," says Kolachalama, "one might have been able to analyze 10 scans a day. Now a computer can do a million scans in a week, and if that computer is learning from those million scans, then it is doing something very interesting."
Kolachalama's lab is also using imaging data and machine learning to explore the progression of kidney disease and osteoarthritis. His kidney disease research, which uses digitized renal biopsy data obtained from BMC, is conducted with a team of nephrologists that includes Vipul Chitalia, a MED associate professor of medicine. Algorithms for that project will predict the amount of life left within a kidney after the patient comes to the hospital and undergoes a biopsy.
Kolachalama also collaborates with Rhoda Au, a MED professor of anatomy and neurobiology and neurology and the head of neuropsychology at the Framingham Heart Study, a seven-decade-long collection of data run by Boston University and the National Heart, Lung, and Blood Institute (NHLBI). Au, whose work was recently lauded in Bill Gates' blog gatesnotes, is looking for biometric clues to Alzheimer's disease onset in several human behaviors. She is combing through thousands of audio files of patients recorded during health assessments, using advanced machine learning to analyze changes in linguistic and acoustic features, such as pauses, hesitation, word frequency, and pitch, which may be early indicators of disease. And in a broader effort, she has created a digital brain health–monitoring platform that via wearable devices tracks such things as sleep, walking gait, and balance.
"We are capturing a range of behaviors associated with the risk of disease," says Au. "We are monitoring them so we can detect it much earlier than we can do right now."
Elaine Nsoesie, an SPH assistant professor of global health, is working with Margrit Betke, a CAS professor of computer science, on an analysis of four million Instagram images posted from Kenya. They are looking for evidence that Kenyans are swapping their native diet for western foods. Photo by Katherine Taylor
Clues in Social Media
While Kolachalama goes small, Elaine Nsoesie is thinking big. The School of Public Health assistant professor of global health is using geotagged tweetsto study various aspects of health in different neighborhoods. Nsoesie, a Data Science Faculty Fellow in the Data Science Initiative at the Hariri Institute for Computing and Computational Science & Engineering, was part of a team that mapped 80 million geotagged tweets from more than 600,000 Twitter users to census tracts and zip codes across the United States to develop indicators of happiness, food, and physical activity. She and SPH postdoctoral associate Nina Cesare hope to better understand how discussions of health behaviors on social media differ across demographic groups. They believe that their social media data can lead to new and better ways to assess health indicators in communities across the United States. After all, she says, that information is timely, and collecting it is much less costly than using surveys.
Nsoesie is also working with Margrit Betke, a College of Arts & Sciences professor of computer science, on a project aimed at understanding Kenyan dietary preferences. She and Betke are analyzing four million Instagram images posted by people in the East African country, and hoping to learn if, and in what parts of the country, Kenyans prefer discussing western foods to their native diets. One challenge, says Betke, has been teaching computers to recognize African foods. When they can do that, she says, they can begin to learn how "green" or how "greasy" the Kenyan diet is, and if it varies in urban and rural areas.
Like Nsoesie, Betke works simultaneously on several health-related research projects. One of her most promising, conducted with Terry Ellis (MED'05), a Sargent College of Health & Rehabilitation Sciences assistant professor of physical therapy and director of the Center for Neurorehabilitation, provides home-based physical therapy support to people with Parkinson's disease. A camera-based AI system in the patient's home tracks the reach and speed of the patient's movements and compares them to ideal parameters. Software then sends an assessment to a healthcare provider, who can advise the patient to move faster or slower or further extend their movements.
All because the data told them to.
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SENIOR CONTRIBUTING EDITOR
Art Jahnke began his career at the Real Paper, a Boston area alternative weekly. He has worked as a writer and editor at Boston Magazine, web editorial director at CXO Media, and executive editor in Marketing & Communications at Boston University, where his work was honored with many awards. Profile
How to Make Self-Driving Vehicles Smarter, Bolder
With $7.5M DOD grant, BU researchers head international team developing bioinspired control systems for self-navigated vehicles
By Kat J. McAlpine (originally published on BU Today, April 17, 2019)
Autonomous vehicles that can maneuver themselves around any city are already out on our public roads, says Yannis Paschalidis, but operating off-road remains a challenge.
"These vehicles are designed for very structured environments, within roads and lanes," says Paschalidis, a College of Engineering professor of biomedical, systems, and electrical and computer engineering, who uses data science and machine learning to develop new software algorithms and control systems. "They are only programmed to recognize a small number of different types of objects."
Paschalidis has a vision for self-driving vehicles that would launch them from the mundane world of suburban commuting to the most dynamic (and sometimes harsh) places around the globe. "We are interested in developing fundamental principles that can be applied to autonomous vehicles capable of navigating themselves on the ground, underwater, and in the air," he says.
To make that possible, the Department of Defense has awarded $7.5 million in Multidisciplinary University Research Initiative (MURI) funding for Paschalidis to team up with other scientists from Boston University, Massachusetts Institute of Technology, and Australian research universities.
"Our team spans two continents and brings together some of the preeminent experts in neuroscience—with emphasis on localization, mapping, and navigation functions—with experts in robotics, computer vision, control systems, and algorithms," says Paschalidis, the team's principal investigator. "We're essentially going to use insights from neuroscience to better organize and control engineered systems."
Their goal? To investigate how the brains of living organisms—namely ants, animals, and humans—process their spatial environments to derive meaningful navigation information. The international research team calls their efforts Project NeuroAutonomy.
"The research that we'll be doing under this MURI is focused on the most interesting control system out there—the brain and its coordination of the neurosensory and neuromuscular systems in the body," says co–principal investigator John Baillieul, an ENG Distinguished Professor of mechanical, systems, and electrical and computer engineering.
The Australian collaborators, particularly insect navigation expert Ken Cheng of Macquarie University, will draw insight from the way that ants use visual cues to move around. In the United States, BU collaborators will lead teams that examine animal and human spatial navigation.
"This project offers the potential for some major theoretical breakthroughs for understanding cognition," says co–principal investigator Michael Hasselmo, director of BU's Center for Systems Neuroscience and a College of Arts & Sciences professor of psychological and brain sciences.
Hasselmo will lead the team's investigation of how rodents navigate their environment. He says that although this project is focusing on navigation, elements of the algorithm the team plans to develop could eventually be applied "to a broad range of different types of intelligent behavior."
To develop the algorithm, the team will zero in on three big gaps between the navigation prowess of current autonomous vehicle technology and biological organisms, says co–principal investigator Chantal Stern, director of BU's Cognitive Neuroimaging Center. Stern will lead team members in using functional MRI to investigate how humans develop a map of their environment and detect changing elements of their surroundings.
"An example that comes directly from robotics is known as the loop closure problem," Stern says. "When you wander around in a circle through your house and come back to the kitchen, you know you are back in the kitchen; you have mapped your environment and recognize that you have returned to a location you were in before. In robotics, that's a difficult problem for an autonomous system. An autonomous system will keep mapping a location it returns to, in the same way a Roomba vacuum keeps cleaning the same spot when it comes back around to the same location."
Having a fully autonomous vehicle accurately map an area of land or water could be a useful application for military operations, allowing foreign landscapes to be charted without human assistance or putting any lives at risk.
"If you want to [use autonomous vehicles to] develop an accurate map of an area, then you don't want [the vehicle] to overwrite the map every time [it returns] to the same place," says Stern, a CAS professor of psychological and brain sciences.
Her team will also investigate how humans decide what's valuable information and what's visual clutter as they navigate their environment.
"How do you determine what is a landmark? For example, we can use the Citgo sign as a landmark, but we don't tell people to turn right at the UPS truck," Stern says. "The UPS truck is not a useful navigational landmark." In other words, because it's not a stable part of the environment, the UPS truck is just visual clutter.
The last problem the team will investigate is how we predict the changing dynamics of an environment.
"When you are driving down the street and see a child or a dog or bicyclist on the side of the road, you are already thinking that they might cross the street and you'll be prepared for [them] to move," Stern says. "But you know the mailbox isn't going to move. How does the brain do that? How do you understand that prediction?"
John Leonard, a co–principal investigator and the head of MIT's Marine Robotics group, says he's looking forward to making use of recent advances in deep learning and object detection.
"Taking the best from biologically inspired models and biologically derived models and combining those with real robot experiments is very exciting," Leonard says. "The potential impact of the research is awe-inspiring. The fact that memory formation is coupled to how an animal or human knows their position…perhaps could one day lead to better insights that ultimately might lead to better therapies for memory."
Paschalidis, Project NeuroAutonomy's team leader, predicts the biggest challenge for the team is that it will be impossible to read the "code" that animals and humans use to navigate.
"We have to infer that code from observations that we make," he says. "The second challenge will be to translate those observations into specific, detailed control policies [for autonomous vehicles]."
The US-based team also includes Margrit Betke, a CAS professor of computer science, Roberto Tron, an ENG assistant professor of mechanical engineering, and from MIT, Nicholas Roy, a professor of aeronautics and astronautics.
Kat J. McAlpine can be reached at katjmcal@bu.edu.
BU-led Research Team Wins Competitive $7.5 million MURI Grant to Create Neuro-Autonomous Robots
By Maureen Stanton, CISE
Dream Team of Engineers, Computer Scientists, and Neuroscientists from BU, MIT, and Australia to develop neuro-inspired capabilities for Land, Sea, and Air-based Autonomous Robots
A Boston University-led research team was selected to receive a $7.5 million Multidisciplinary University Research Initiative (MURI) grant from the U.S. Department of Defense (DoD). With this prestigious grant, the researchers will develop a novel category of neuro-inspired autonomous robots for land, sea, and air that the investigators have termed "neuro-autonomous."
The initiative will tackle the challenge of how to make robots truly autonomous as well as provide important insights into the process of learning and memory formation. The project will be led by Yannis Paschalidis, director of the Center for Information and Systems Engineering, and professor in the College of Engineering at Boston University.
The winning team of researchers is a Dream Team of experts from Boston University and the Massachusetts Institute of Technology. It includes experts in neuroscience, robotics, computer science, computer vision, artificial intelligence, mathematical systems theory, and a host of other related advanced technology domains. The project will also benefit from collaboration with renowned researchers from the University of Melbourne, Macquarie University, Queensland University of Technology, and the University of New South Wales. (See researcher biographies.)
Yannis Paschalidis, Director, BU Center for Information & Systems Engineering
"Clearly, humans and animals are more effective at navigating than robots are," said Professor Paschalidis. "While there have been tremendous advances in intelligent systems, state-of-the-art autonomous systems are still limited in that they are programmed for specific objectives in well-structured environments. Our research focus is to create a new class of neuro-autonomous robots, inspired by the fusion of multiple sensor modalities, spatial awareness, and spatial memory inherent in biological organisms. These systems will have unprecedented capabilities for self-learning and on-the-fly adaptation to environmental novelty, enabling their ability to pursue complex goals in highly dynamic environments."
Advancing a Science of Autonomy
Under this MURI grant entitled "Neuro-Autonomy: Neuroscience-Inspired Perception, Navigation, and Spatial Awareness for Autonomous Robots," the research team will conduct experiments to gain insights into how humans and animals process visual and other stimuli to engage in goal-directed navigation. With this greater understanding, the researchers will develop new algorithmic methods that can do the same for robots, enabling them to navigate autonomously. The implementation of the core algorithms will be agnostic to the specific robot used, enabling validation studies with ground, aerial, and underwater autonomous robots using experimental testbeds at BU, MIT, and Australian (AUS) institutions.
"This is fascinating science," adds Professor Paschalidis. "We will develop the ability to make behavioral observations of animals and humans, correlate behavior with activity in the brain, and use the data to design control policies that will guide autonomous systems. This truly is the next frontier in advancing the field of robotics and autonomous vehicles."
Cross-disciplinary Global Investigator Team
This MURI grant makes possible the multidisciplinary collaboration of an extraordinary team of researchers who bridge the neuroscience, engineering, and computer science worlds. The team spans two continents and brings together some of the pre-eminent experts in neuroscience, with emphasis on localization, mapping, and navigation functions, with experts in robotics, computer vision, control systems, and algorithms.
The BU research team includes BU College of Engineering Professors John Baillieul, Yannis Paschalidis, and Roberto Tron, computer science Professor Margrit Betke, as well as neuroscience Professors Michael Hasselmo and Chantal Stern of the College of Arts and Sciences. The Massachusetts Institute of Technology is a subaward recipient on this grant. MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) faculty John Leonard, Samuel C. Collins Professor of Mechanical and Ocean Engineering, and Nicholas Roy, Bisplinghoff Professor of Aeronautics and Astronautics, are the co-principal investigators.
Remarks from Principal Investigators
Michael Hasselmo, Director, BU Center for Systems Neuroscience
"While there are some very exciting computational theories, computational neuroscience has not yet fully accounted for the mechanisms and the function of all these interesting experimental phenomena," says Professor Hasselmo, director of the Center for Systems Neuroscience at Boston University. "This project offers an exciting opportunity to collaborate with engineering in order to provide the mathematical and theoretical framework necessary. Further, this project offers the potential for some major theoretical breakthroughs for understanding cognition. While we will be focusing on navigation, elements of the algorithms we will develop could apply to a broad range of different types of intelligent behavior."
John Leonard, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
"As a roboticist, I am excited about combining insights into how scientists think the brain might work to make better robots and to exploit some of the recent advances in things like deep learning and object detection," says Professor Leonard, MIT CSAIL. "Taking the best from biologically-inspired models and combining those with real robot experiments is very exciting. The potential impact of the research is awe-inspiring. We understand now that memory formation is coupled to how an animal or human knows their position; there is a coupling there that perhaps could one day lead to better insights that ultimately might lead to better therapies for memories."
Chantal Stern, Director, BU Cognitive Neuroimaging Center
"The MURI project is a fantastic opportunity for us to combine knowledge and expertise across disciplines," says Professor Stern, Director, BU Cognitive Neuroimaging Center. "As a neuroscientist, I find it fascinating to think about the speed and flexibility of human cognition, and I've learned a tremendous amount about what state-of-the-art robotics can and cannot do from my interactions with roboticists on the MURI team. Spatial navigation is an ideal test bed for thinking about spatial awareness and problem solving across animals and humans, and I'm looking forward to working with the engineering and neuroscience faculty here at BU as well as with our robotics colleagues, John Leonard and Nick Roy, at MIT."
John Baillieul, Distinguished Professor of Engineering, BU
Professor Baillieul, a Distinguished Professor of Engineering at Boston University states, "My career has been devoted to systems and control engineering. The research that we'll be doing under this MURI is focused on the most interesting control system out there–the brain and its coordination of the neurosensory and neuromuscular systems in the body."
Margrit Betke, Co-director, BU Artificial Intelligence Research Initiative
"I am excited to lead the MURI research efforts around the video recordings of animals and their navigation behavior, as we monitor their brain activities," said Professor Betke, co-director of the BU Artificial Intelligence Research Initiative.
Professor Girish Nair at the University of Melbourne will lead the Australian-based researchers. "The Australian team is thrilled to participate in this project," says Professor Nair. "We look forward to a fruitful and exciting collaboration with our partners at Boston University and MIT."
The BU-led MURI is one of 24 MURI awards announced by the DoD on April 3, 2019. The highly competitive MURI program supports basic research in science and engineering at U.S. institutions of higher education and is focused on multidisciplinary research efforts where more than one traditional discipline interacts to provide rapid advances in scientific areas of interest to the DoD.
For details about this ONR MURI grant, see "Project Abstract approved for Public Release."
BU Initiatives on Cities – Beyond Congestion: Pathways to Better Mobility
By Doruntina Zeneli
On Tuesday March 26, panelists discussed how current transportation networks within major cities do not operate efficiently and future technology will serve a key role in incentivizing change and eliminating congestion. The conversation was initially led by Matthew Raifman, Senior Manager at Ford Smart Mobility. Raifman described congestion as an "excess of vehicles on a portion of roadway at a particular time resulting in a reduction below total possible throughput." Traffic congestion serves as a negative externality for residents by inducing vehicle costs, greenhouse gas emissions, additional travel time and potential health risks.
He presented solutions to congestion by advocating for fewer vehicles, building more roads and spreading demand over time. However, cities simply do not have the available space to build more roads and repurposing green spaces, bike lanes and sidewalks, are not an effective solution in solving this ubiquitous issue. Raifman also emphasized that reducing the number of vehicles and implementing workplace policies that stagger arrival times of employees, serve as viable solutions to this issue.
Similarly, Yannis Paschalidis a BU Professor of Engineering, explained the severity of congestion in cities today. It is predicted that the cost of traffic congestion will reach $2.8 trillion by 2030 in the U.S. alone. Additionally, Boston was recently declared as the number one city in hours lost during rush hour traffic for each driver in 2018.
Moreover, Paschalidis showed an interactive model that illustrated congestion in Boston neighborhoods from 2012 to 2015. By providing these reference years, the audience was able to pinpoint increases in congestion within specific areas in Boston. Paschalidis also developed a transportation network model, which utilized a congestion function and demonstrated that drivers make optimally selfish decisions when choosing traffic routes.
Iram Farooq, the Assistant City Manager for the City of Cambridge also spoke about her role in developing policies that focuses on transportation, mobility and sustainability in Cambridge. Her involvement on transportation policies in the early 90's enabled her local community to have manageable traffic levels with small commuting times during rush hour in comparison to Boston.
Jascha Franklin-Hodge, the former CIO for the City of Boston, emphasized the importance of new mobility as an emerging service industry. Recent services include Zipcar, ride-sharing, public bike-share systems and autonomous vehicles. Although there are benefits from new mobility, the issue of consumer protection arises. Many of these services are publicly traded or privately owned, so there is a potential risk consumers may lose the benefit of a marketplace. For instance, all forms of transportation modes are listed within these mobile apps. Eventually, one to two large companies will gain pricing power over consumers by capitalizing on the marketplace for their basic mobility needs. As a result, standards for consumer protection should be implemented as the industry of new mobility grows.
Click here to download slides from the panel.
BU Proposes to Build Data Sciences Center, Aiming to Become Leader in Booming Field
BU Today reports that Boston University has announced plans to build a new 17-floor Data Sciences Center, furthering its leadership in data science and interdisciplinary research.
Doug Most, Assistant VP, Executive Editor of BU Todaywrites, "Big data is playing a particularly major role in healthcare, allowing hospitals and doctors to move toward more personalized medicine."
The story references related research initiatives of CISE affiliated faculty Yannis Paschalidis (ECE, BME, SE) and Christos Cassandras (ECE, SE) and BMC Associate Chief Medical Information Officer and BU School of Medicine Assistant Professor of Medicine Rebecca Mishuris.
Using Big Data, Machine Learning to Reduce Chronic Disease Spending
Research by College of Engineering Professors Yannis Paschalidis (ECE, BME, SE) and Christos Cassandras (ECE, SE) and BMC Associate Chief Medical Information Officer and BU School of Medicine Assistant Professor of Medicine Rebecca Mishuris is featured in HealthIT Analytics.
The article discusses how they will use a new $900K grant from the National Science Foundation to advance machine learning and big data to reduce healthcare spending on chronic conditions, including diabetes and heart disease.
Researchers Win $900k NSF Grant to Predict Heart Disease, Diabetes Using Machine Learning
Researchers (from left) Paschalidis, Mishuris, and Cassandras win $900K NSF grant to predict heart disease and diabetes using machine learning.
Researchers from the Boston University College of Engineering and Boston Medical Center (BMC) will use a three-year, $900,000 grant from the National Science Foundation to develop and pilot a health informatics system to predict patients at risk of heart disease or diabetes, and enable early intervention and personalized treatment.
"Our research vision is to deliver personalized healthcare, from prediction to diagnostics to population health management," said Professor Ioannis Paschalidis (ECE, BME, SE), director of the Center for Information & Systems Engineering (CISE). "In earlier work, we demonstrated how machine learning could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82 percent, a significant improvement over existing risk models — such as the Framingham study for cardiovascular disease. Now, with our synergistic team of scientists and physicians, we are developing more robust predictive methods and capabilities for offering personalized recommendations that can guide physicians and patients as they make health-related decisions. This is a new frontier in medical informatics research and we expect it will impact medical practice."
Diagnosing these chronic diseases requires complex sets of clinical and pathological data, which often are not comprehensive, consistent, nor up to date for treating physicians. The result is that patients at higher risk often don't get needed treatment, while those at lower risk do, leading to poor patient outcomes and unnecessary costs. The new, interdisciplinary research collaboration will be led by Paschalidis, CISE member Professor Christos Cassandras (ECE, SE) and BMC Associate Chief Medical Information Officer and BU School of Medicine Assistant Professor of Medicine Rebecca Mishuris. They will develop a new generation of predictive methods based on supervised machine learning techniques that are interpretable, have higher predictive power, and can handle more data.
The researchers will develop an approach based on novel mathematical methods and the requisite algorithms where electronic health records and real-time health data — including wearable, implantable, and home-based, networked diagnostic devices – can be used to develop prediction analytics that anticipate future events such as hospitalizations, re-admissions, and transitioning to an acute stage of a disease. These predictions trigger personalized interventions, ranging from increased monitoring and doctor visits to optimized treatment policies adapted to each patient.
The researchers will also focus on enabling personalized treatment based on learning and optimizing treatment protocols for chronic diseases. "Protocols of this type are typically empirical, using a one-size-fits-all approach," explained Cassandras, head of the Systems Engineering Division. "They assess the stage of the disease and adapt medication based on the stage but not the patient. By incorporating specific personalized interventions with recommendations, clinicians can, therefore, intervene before a patient's condition reaches a critical phase."
In addition to methodological and algorithmic development, the project will pilot the newly developed algorithms by integrating them into the electronic health record system at BMC and with 14 affiliated Community Health Centers.
"Personalized, predictive healthcare is the future of medicine," said Dr. Mishuris. "Our research is geared toward providing clinicians with powerful, interpretable data to achieve that goal. Physicians are drowning in data and administrative processes. Our research approach will help physicians manage the deluge of clinical and patient data to make decisions in a more systematic fashion."
Lectures in China
Professor Paschalidis will be visiting China in mid July 2018 to deliver a semi-plenary talk at the 2018 MTNS Conference and invited seminars at the Shanghai Jia Tong University (SJTU flyer) and the Huazhong University of Science and Technology.
The Price of Anarchy on the roads and how to reduce it
We are publishing a new paper in the Proceedings of the IEEE which used minute-by-minute traffic data from the Boston area to estimate the effect on traffic congestion of drivers' selfish route selection as opposed to a more coordinated, socially optimal routing scheme. We found that during certain very congested periods, socially optimal routing can lead to as much as 50% reduction in congestion. This work is in collaboration with my colleague Christos Cassandras and two of our students, Jing Zhang and Sepideh Pourazarm. It was completed while I was on sabbatical at MIT.
A press release was released by MIT Sloan and KPCC, the southern California NPR station, aired a segment and posted a story on this work.
A related story appeared on the web site of the Center for Information and Systems Engineering.
We are grateful to the Boston region Metropolitan Planning Organization and to the City of Boston for providing access to the traffic data that made this work possible.
Our paper on ``Federated learning of predictive models from federated Electronic Health Records''...
Big Data, Big Impact Part Four of a Five-Part Series Part Five → Story by Art Jahnke Illustration...
With $7.5M DOD grant, BU researchers head international team developing bioinspired control...
By Maureen Stanton, CISE Dream Team of Engineers, Computer Scientists, and Neuroscientists from BU,
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