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Marshaling artificial intelligence in the fight against Covid-19 | MIT News

Artificial intelligence could play a decisive role in stopping the Covid-19 pandemic. To give the technology a push, the MIT-IBM Watson AI Lab is funding 10 projects at MIT aimed at advancing AI’s transformative potential for society. The research will target the immediate public health and economic challenges of this moment. But it could have a lasting impact on how we evaluate and respond to risk long after the crisis has passed. The 10 research projects are highlighted below.

Early detection of sepsis in Covid-19 patients 

Sepsis is a deadly complication of Covid-19, the disease caused by the new coronavirus SARS-CoV-2. About 10 percent of Covid-19 patients get sick with sepsis within a week of showing symptoms, but only about half survive.

Identifying patients at risk for sepsis can lead to earlier, more aggressive treatment and a better chance of survival. Early detection can also help hospitals prioritize intensive-care resources for their sickest patients. In a project led by MIT Professor Daniela Rus, researchers will develop a machine learning system to analyze images of patients’ white blood cells for signs of an activated immune response against sepsis.

Designing proteins to block SARS-CoV-2

Proteins are the basic building blocks of life, and with AI, researchers can explore and manipulate their structures to address longstanding problems. Take perishable food: The MIT-IBM Watson AI Lab recently used AI to discover that a silk protein made by honeybees could double as a coating for quick-to-rot foods to extend their shelf life.

In a related project led by MIT professors Benedetto Marelli and Markus Buehler, researchers will enlist the protein-folding method used in their honeybee-silk discovery to try to defeat the new coronavirus. Their goal is to design proteins able to block the virus from binding to human cells, and to synthesize and test their unique protein creations in the lab.

Saving lives while restarting the U.S. economy

Some states are reopening for business even as questions remain about how to protect those most vulnerable to the coronavirus. In a project led by MIT professors Daron Acemoglu, Simon Johnson and Asu Ozdaglar will model the effects of targeted lockdowns on the economy and public health.

In a recent working paper co-authored by Acemoglu, Victor Chernozhukov, Ivan Werning, and Michael Whinston, MIT economists analyzed the relative risk of infection, hospitalization, and death for different age groups. When they compared uniform lockdown policies against those targeted to protect seniors, they found that a targeted approach could save more lives. Building on this work, researchers will consider how antigen tests and contact tracing apps can further reduce public health risks.

Which materials make the best face masks?

Massachusetts and six other states have ordered residents to wear face masks in public to limit the spread of coronavirus. But apart from the coveted N95 mask, which traps 95 percent of airborne particles 300 nanometers or larger, the effectiveness of many masks remains unclear due to a lack of standardized methods to evaluate them.

In a project led by MIT Associate Professor Lydia Bourouiba, researchers are developing a rigorous set of methods to measure how well homemade and medical-grade masks do at blocking the tiny droplets of saliva and mucus expelled during normal breathing, coughs, or sneezes. The researchers will test materials worn alone and together, and in a variety of configurations and environmental conditions. Their methods and measurements will determine how well materials protect mask wearers and the people around them.

Treating Covid-19 with repurposed drugs

As Covid-19’s global death toll mounts, researchers are racing to find a cure among already-approved drugs. Machine learning can expedite screening by letting researchers quickly predict if promising candidates can hit their target.

In a project led by MIT Assistant Professor Rafael Gomez-Bombarelli, researchers will represent molecules in three dimensions to see if this added spatial information can help to identify drugs most likely to be effective against the disease. They will use NASA’s Ames and U.S. Department of Energy’s NSERC supercomputers to further speed the screening process.

A privacy-first approach to automated contact tracing

Smartphone data can help limit the spread of Covid-19 by identifying people who have come into contact with someone infected with the virus, and thus may have caught the infection themselves. But automated contact tracing also carries serious privacy risks.

In collaboration with MIT Lincoln Laboratory and others, MIT researchers Ronald Rivest and Daniel Weitzner will use encrypted Bluetooth data to ensure personally identifiable information remains anonymous and secure.

Overcoming manufacturing and supply hurdles to provide global access to a coronavirus vaccine

A vaccine against SARS-CoV-2 would be a crucial turning point in the fight against Covid-19. Yet, its potential impact will be determined by the ability to rapidly and equitably distribute billions of doses globally. This is an unprecedented challenge in biomanufacturing. 

In a project led by MIT professors Anthony Sinskey and Stacy Springs, researchers will build data-driven statistical models to evaluate tradeoffs in scaling the manufacture and supply of vaccine candidates. Questions include how much production capacity will need to be added, the impact of centralized versus distributed operations, and how to design strategies for fair vaccine distribution. The goal is to give decision-makers the evidence needed to cost-effectively achieve global access.

Leveraging electronic medical records to find a treatment for Covid-19

Developed as a treatment for Ebola, the anti-viral drug remdesivir is now in clinical trials in the United States as a treatment for Covid-19. Similar efforts to repurpose already-approved drugs to treat or prevent the disease are underway.

In a project led by MIT professors Roy Welsch and Stan Finkelstein, researchers will use statistics, machine learning, and simulated clinical drug trials to find and test already-approved drugs as potential therapeutics against Covid-19. Researchers will sift through millions of electronic health records and medical claims for signals indicating that drugs used to fight chronic conditions like hypertension, diabetes, and gastric influx might also work against Covid-19 and other diseases.

Finding better ways to treat Covid-19 patients on ventilators 

Troubled breathing from acute respiratory distress syndrome is one of the complications that brings Covid-19 patients to the ICU. There, life-saving machines help patients breathe by mechanically pumping oxygen into the lungs. But even as towns and cities lower their Covid-19 infections through social distancing, there remains a national shortage of mechanical ventilators and serious health risks of ventilation itself.

In collaboration with IBM researchers Zach Shahn and Daby Sow, MIT researchers Li-Wei Lehman and Roger Mark will develop an AI tool to help doctors find better ventilator settings for Covid-19 patients and decide how long to keep them on a machine. Shortened ventilator use can limit lung damage while freeing up machines for others. To build their models, researchers will draw on data from intensive-care patients with acute respiratory distress syndrome, as well as Covid-19 patients at a local Boston hospital.

Returning to normal via targeted lockdowns, personalized treatments, and mass testing

In a few short months, Covid-19 has devastated towns and cities around the world. Researchers are now piecing together the data to understand how government policies can limit new infections and deaths and how targeted policies might protect the most vulnerable.

In a project led by MIT Professor Dimitris Bertsimas, researchers will study the effects of lockdowns and other measures meant to reduce new infections and deaths and prevent the health-care system from being swamped. In a second phase of the project, they will develop machine learning models to predict how vulnerable a given patient is to Covid-19, and what personalized treatments might be most effective. They will also develop an inexpensive, spectroscopy-based test for Covid-19 that can deliver results in minutes and pave the way for mass testing. The project will draw on clinical data from four hospitals in the United States and Europe, including Codogno Hospital, which reported Italy’s first infection.

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The MIT Press and UC Berkeley launch Rapid Reviews: COVID-19 | MIT News

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The MIT Press has announced the launch of Rapid Reviews: COVID-19 (RR:C19), an open access, rapid-review overlay journal that will accelerate peer review of Covid-19-related research and deliver real-time, verified scientific information that policymakers and health leaders can use.

Scientists and researchers are working overtime to understand the SARS-CoV-2 virus and are producing an unprecedented amount of preprint scholarship that is publicly available online but has not been vetted yet by peer review for accuracy. Traditional peer review can take four or more weeks to complete, but RR:C19’s editorial team, led by Editor-in-Chief Stefano M. Bertozzi, professor of health policy and management and dean emeritus of the School of Public Health at the University of California at Berkeley, will produce expert reviews in a matter of days.

Using artificial intelligence tools, a global team will identify promising scholarship in preprint repositories, commission expert peer reviews, and publish the results on an open access platform in a completely transparent process. The journal will strive for disciplinary and geographic breadth, sourcing manuscripts from all regions and across a wide variety of fields, including medicine; public health; the physical, biological, and chemical sciences; the social sciences; and the humanities. RR:C19 will also provide a new publishing option for revised papers that are positively reviewed.

Amy Brand, director of the MIT Press sees the no-cost open access model as a way to increase the impact of global research and disseminate high-quality scholarship. “Offering a peer-reviewed model on top of preprints will bring a level of diligence that clinicians, researchers, and others worldwide rely on to make sound judgments about the current crisis and its amelioration,” says Brand. “The project also aims to provide a proof-of-concept for new models of peer-review and rapid publishing for broader applications.”

Made possible by a $350,000 grant from the Patrick J. McGovern Foundation and hosted on PubPub, an open-source publishing platform from the Knowledge Futures Group for collaboratively editing and publishing journals, monographs, and other open access scholarly content, RR:C19 will limit the spread of misinformation about Covid-19, according to Bertozzi.

“There is an urgent need to validate — or debunk — the rapidly growing volume of Covid-19-related manuscripts on preprint servers,” explains Bertozzi. “I’m excited to be working with the MIT Press, the Patrick J. McGovern Foundation, and the Knowledge Futures Group to create a novel publishing model that has the potential to more efficiently translate important scientific results into action. We are also working with COVIDScholar, an initiative of UC Berkeley and Lawrence Berkeley National Lab, to create unique AI/machine learning tools to support the review of hundreds of preprints per week.”

“This project signals a breakthrough in academic publishing, bringing together urgency and scientific rigor so the world’s researchers can rapidly disseminate new discoveries that we can trust,” says Vilas Dhar, trustee of the Patrick J. McGovern Foundation. “We are confident the RR:C19 journal will quickly become an invaluable resource for researchers, public health officials, and healthcare providers on the frontline of this pandemic. We’re also excited about the potential for a long-term transformation in how we evaluate and share research across all scientific disciplines.”

On the collaboration around this new journal, Travis Rich, executive director of the Knowledge Futures Group notes, “At a moment when credibility is increasingly crucial to the well-being of society, we’re thrilled to be partnering with this innovative journal to expand the idea of reviews as first-class research objects, both on PubPub and as a model for others.

RR:C19 will publish its first reviews in July 2020 and is actively recruiting potential reviewers and contributors. To learn more about this project and its esteemed editorial board, visit rapidreviewscovid19.mitpress.mit.edu.

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Faculty receive funding to develop artificial intelligence techniques to combat Covid-19 | MIT News

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Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

“We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.”

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

“The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.”

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards: 

“Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions” — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

“Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data” — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

“Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic” — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

“Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak” — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

“Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections” — David Gifford, professor of biological engineering and of electrical engineering and computer science

“Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19” — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

“Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic” — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor

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