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Bringing the predictive power of artificial intelligence to health care | MIT News

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An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital — before they to return to the doctor’s office with further complications.

But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that aren’t optimized for their patients.

Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.

The platform is being used to help hospitals determine which patients are most likely to miss appointments, acquire infections like sepsis, benefit from periodic check ups, and more. Health insurers, in turn, are using ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.

“We built a health care data science platform that can take in whatever data an organization has, quickly build models that are specific to [their patients], and deploy those models,” says ClosedLoop co-founder and Chief Technology Officer Dave DeCaprio ’94. “Being able to take somebody’s data the way it lives in their system and convert that into a model that can be readily used is still a problem that requires a lot of [health care] domain knowledge, and that’s a lot of what we bring to the table.”

In light of the Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for patient surges. The open source tool, called the C-19 Index, has been used to connect high-risk patients with local resources and helped health care systems create risk scores for tens of millions of people overall.

The index is just the latest way that ClosedLoop is accelerating the health care industry’s adoption of AI to improve patient health, a goal DeCaprio has worked toward for the better part of his career.

Designing a strategy

After working as a software engineer for several private companies through the internet boom of the early 2000s, DeCaprio was looking to make a career change when he came across a project focused on genome annotation at the Broad Institute of MIT and Harvard.

The project was DeCaprio’s first professional exposure to the power of artificial intelligence. It blossomed into a six year stint at the Broad, after which he continued exploring the intersection of big data and health care.

“After a year in health care, I realized it was going to be really hard to do anything else,” DeCaprio says. “I’m not going to be able to get excited about selling ads on the internet or anything like that. Once you start dealing with human health, that other stuff just feels insignificant.”

In the course of his work, DeCaprio began noticing problems with the ways machine learning and other statistical techniques were making their way into health care, notably in the fact that predictive models were being applied without regard for hospitals’ patient populations.

“Someone would say, ‘I know how to predict diabetes’ or ‘I know how to predict readmissions,’ and they’d sell a model,” DeCaprio says. “I knew that wasn’t going to work, because the reason readmissions happen in a low-income population of New York City is very different from the reason readmissions happen in a retirement community in Florida. The important thing wasn’t to build one magic model but to build a system that can quickly take somebody’s data and train a model that’s specific for their problems.”

With that approach in mind, DeCaprio joined forces with former co-worker and serial entrepreneur Andrew Eye, and started ClosedLoop in 2017. The startup’s first project involved creating models that predicted patient health outcomes for the Medical Home Network (MHN), a not-for-profit hospital collaboration focused on improving care for Medicaid recipients in Chicago.

As the founders created their modeling platform, they had to address many of the most common obstacles that have slowed health care’s adoption of AI solutions.

Often the first problems startups run into is making their algorithms work with each health care system’s data. Hospitals vary in the type of data they collect on patients and the way they store that information in their system. Hospitals even store the same types of data in vastly different ways.

DeCaprio credits his team’s knowledge of the health care space with helping them craft a solution that allows customers to upload raw data sets into ClosedLoop’s platform and create things like patient risk scores with a few clicks.

Another limitation of AI in health care has been the difficulty of understanding how models get to results. With ClosedLoop’s models, users can see the biggest factors contributing to each prediction, giving them more confidence in each output.

Overall, to become ingrained in customer’s operations, the founders knew their analytics platform needed to give simple, actionable insights. That has translated into a system that generates lists, risk scores, and rankings that care managers can use when deciding which interventions are most urgent for which patients.

“When someone walks into the hospital, it’s already too late [to avoid costly treatments] in many cases,” DeCaprio says. “Most of your best opportunities to lower the cost of care come by keeping them out of the hospital in the first place.”

Customers like health insurers also use ClosedLoop’s platform to predict broader trends in disease risk, emergency room over-utilization, and fraud.

Stepping up for Covid-19

In March, ClosedLoop began exploring ways its platform could help hospitals prepare for and respond to Covid-19. The efforts culminated in a company hackathon over the weekend of March 16. By Monday, ClosedLoop had an open source model on GitHub that assigned Covid-19 risk scores to Medicare patients. By that Friday, it had been used to make predictions on more than 2 million patients.

Today, the model works with all patients, not just those on Medicare, and it has been used to assess the vulnerability of communities around the country. Care organizations have used the model to project patient surges and help individuals at the highest risk understand what they can do to prevent infection.

“Some of it is just reaching out to people who are socially isolated to see if there’s something they can do,” DeCaprio says. “Someone who is 85 years old and shut in may not know there’s a community based organization that will deliver them groceries.”

For DeCaprio, bringing the predictive power of AI to health care has been a rewarding, if humbling, experience.

“The magnitude of the problems are so large that no matter what impact you have, you don’t feel like you’ve moved the needle enough,” he says. “At the same time, every time an organization says, ‘This is the primary tool our care managers have been using to figure out who to reach out to,’ it feels great.”

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Cynthia Breazeal named Media Lab associate director | MIT News

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Cynthia Breazeal has been promoted to full professor and named associate director of the Media Lab, joining the two other associate directors: Hiroshi Ishii and Andrew Lippman. Both appointments are effective July 1.

In her new associate director role, Breazeal will work with lab faculty and researchers to develop new strategic research initiatives. She will also play a key role in exploring new funding mechanisms to support broad Media Lab needs, including multi-faculty research efforts, collaborations with other labs and departments across the MIT campus, and experimental executive education opportunities. 

“I am excited that Cynthia will be applying her tremendous energy, creativity, and intellect to rally the community in defining new opportunities for funding and research directions,” says Pattie Maes, chair of the lab’s executive committee. “As a first step, she has already organized a series of informal charrettes, where all members of the lab community can participate in brainstorming collaborations that range from tele-creativity, to resilient communities, to sustainability and climate change.” 

Most recently, Breazeal has led an MIT collaboration between the Media Lab, MIT Stephen A. Schwarzman College of Computing, and MIT Open Learning to develop aieducation.mit.edu, an online learning site for grades K-12, which shares a variety of online activities for students to learn about artificial intelligence, with a focus on how to design and use AI responsibly. 

While assuming these new responsibilities, Breazeal will continue to head the lab’s Personal Robots research group, which focuses on developing personal social robots and their potential for meaningful impact on everyday life — from educational aids for children, to pediatric use in hospitals, to at-home assistants for the elderly.

Breazeal is globally recognized as a pioneer in human-robot interaction. Her book, “Designing Sociable Robots” (MIT Press, 2002), is considered pivotal in launching the field. In 2019 she was named an AAAI fellow. Previously, she received numerous awards including the National Academy of Engineering’s Gilbreth Lecture Award and MIT Technology Review’s TR100/35 Award. Her robot Jibo was on the cover of TIME magazine in its Best Inventions list of 2017, and in 2003 she was a finalist for the National Design Awards in Communications Design. In 2014, Fortune magazine recognized her as one of the Most Promising Women Entrepreneurs. The following year, she was named one of Entrepreneur magazine’s Women to Watch.

Breazeal earned a BS in electrical and computer engineering from the University of California at Santa Barbara, and MS and ScD degrees from MIT in electrical engineering and computer science.

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Identifying a melody by studying a musician’s body language | MIT News

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We listen to music with our ears, but also our eyes, watching with appreciation as the pianist’s fingers fly over the keys and the violinist’s bow rocks across the ridge of strings. When the ear fails to tell two instruments apart, the eye often pitches in by matching each musician’s movements to the beat of each part. 

A new artificial intelligence tool developed by the MIT-IBM Watson AI Lab leverages the virtual eyes and ears of a computer to separate similar sounds that are tricky even for humans to differentiate. The tool improves on earlier iterations by matching the movements of individual musicians, via their skeletal keypoints, to the tempo of individual parts, allowing listeners to isolate a single flute or violin among multiple flutes or violins. 

Potential applications for the work range from sound mixing, and turning up the volume of an instrument in a recording, to reducing the confusion that leads people to talk over one another on a video -conference calls. The work will be presented at the virtual Computer Vision Pattern Recognition conference this month.

“Body keypoints provide powerful structural information,” says the study’s lead author, Chuang Gan, an IBM researcher at the lab. “We use that here to improve the AI’s ability to listen and separate sound.” 

In this project, and in others like it, the researchers have capitalized on synchronized audio- video tracks to recreate the way that humans learn. An AI system that learns through multiple sense modalities may be able to learn faster, with fewer data, and without humans having to add pesky labels to each real-world representation. “We learn from all of our senses,” says Antonio Torralba, an MIT professor and co-senior author of the study. “Multi-sensory processing is the precursor to embodied intelligence and AI systems that can perform more complicated tasks.”

The current tool, which uses body gestures to separate sounds, builds on earlier work that harnessed motion cues in sequences of images. Its earliest incarnation, PixelPlayer, let you click on an instrument in a concert video to make it louder or softer. An update to PixelPlayer allowed you to distinguish between two violins in a duet by matching each musician’s movements with the tempo of their part. This newest version adds keypoint data, favored by sports analysts to track athlete performance, to extract finer grained motion data to tell nearly identical sounds apart.

The work highlights the importance of visual cues in training computers to have a better ear, and using sound cues to give them sharper eyes. Just as the current study uses musician pose information to isolate similar-sounding instruments, previous work has leveraged sounds to isolate similar-looking animals and objects. 

Torralba and his colleagues have shown that deep learning models trained on paired audio- video data can learn to recognize natural sounds like birds singing or waves crashing. They can also pinpoint the geographic coordinates of a moving car from the sound of its engine and tires rolling toward, or away from, a microphone. 

The latter study suggests that sound-tracking tools might be a useful addition in self-driving cars, complementing their cameras in poor driving conditions. “Sound trackers could be especially helpful at night, or in bad weather, by helping to flag cars that might otherwise be missed,” says Hang Zhao, PhD ’19, who contributed to both the motion and sound-tracking studies.

Other authors of the CVPR music gesture study are Deng Huang and Joshua Tenenbaum at MIT.

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