The MIT-IBM Watson AI Lab is funding 10 research projects aimed at addressing the health and economic consequences of the pandemic.
copyright by news.mit.edu
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.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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. […]
read more – news.mit.edu
The MIT-IBM Watson AI Lab is funding 10 research projects aimed at addressing the health and economic consequences of the pandemic.
copyright by news.mit.edu
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.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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. […]
read more – news.mit.edu
Share this: