Can Donald Trump gain enough black voters to make a difference in 2020?
Mental health clinicians typically use a patient’s answers to specific questions about lifestyle, mood, or past mental illness to diagnose depression. But in a September conference paper, researchers at the Massachusetts Institute of Technology described a new artificial intelligence model they say can predict whether a person is depressed, based solely on raw text and audio from patient interviews, regardless of the topic of conversation.
“The first hints we have that a person is happy, excited, sad, or has some serious cognitive condition, such as depression, is through their speech,” said study co-author Tuka Alhanai, a researcher in MIT’s Computer Science and Artificial Intelligence Laboratory, in a press release.
The researchers trained the artificial intelligence model on a series of 142 audio, text, and video interviews of patients, not all of whom were depressed. The model gradually learned to associate certain speech patterns for people with depression. Its key innovation, according to the researchers, is the ability to detect patterns associated with depression in new individuals without any other diagnostic information.
“The model sees sequences of words or speaking style, and determines that these patterns are more likely to be seen in people who are depressed or not depressed,” Alhanai said. “Then, if it sees the same sequences in new subjects, it can predict if they’re depressed too.”
The MIT scientists hope technology like theirs could lead to computer apps that help people monitor their own mental health. But they believe it would also be effective helping doctors identify mental distress in regular conversations with patients.
“Every patient will talk differently, and if the model sees changes maybe it will be a flag to the doctors,” said co-author James Glass, a senior research scientist at MIT.
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Older Americans who don’t need in-home care or assisted living may still need help with everyday needs such as transportation, shopping, and technology. Many just need companionship. A Miami-based startup called Papa is aiming to fill this gap by providing a service that connects senior citizens with college students, an approach it calls “grandkids-on-demand.”
“People don’t always want to say they want companionship, even though their families say they do,” Papa founder Andrew Parker told TechCrunch. “But when a visit ends up being six hours, that’s evident what it’s for.”
Parker started the company after trying to help his struggling grandmother care for his grandfather, who had Alzheimer’s. Traditional home care didn’t seem to fit his grandfather’s needs, so he looked for an alternative. Hiring a college student to help his grandfather was such a success that he built an app to extend the concept to others. He named the service “Papa” after his grandfather’s nickname.
The basic service is $20 per hour. If seniors pay an additional $30 monthly service fee, they can request a particular student, called a “Papa Pal.” All Papa Pal candidates must pass a background check and motor vehicle records check. The company also administers a personality test to ensure all Papa Pals are outgoing, empathetic, and patient.
Parker trains the students to help their clients increase their social interaction through what he calls “instructive companionship.”
“It’s like, teach your senior how to use social media today, get them their first 10 friends, or have them FaceTime with family members,” Parker told Fast Company.
The startup plans to expand from Florida to at least five other states next year. Medicare will introduce its expanded Medicare Advantage program in 2019 that will cover many of the services Papa offers, such as transportation to medical appointments.
Birds are an aviation hazard. A collision with a flock of birds can damage jet engines, potentially causing an aircraft to crash. The potential for a bird strike is greatest at airports, which offer large, open tracts of land that may tempt birds as a suitable roosting habitat.
Now, engineers at the California Institute of Technology have developed a software algorithm that can enable an ordinary commercial drone to herd a flock of birds away from the crowded airspace of an airport. The researchers formulated a mathematical model of how birds maintain formations and respond to threats along the edge of the flock.
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Magnetic resonance imaging, the gold standard of modern diagnostic imaging, provides doctors with greater detail than other imaging techniques, such as X-ray or CT scans. But MRI scans take a long time—anywhere from 15 minutes to an hour or more. All the while, the patient must lie still inside a claustrophobia-inducing metal tube.
That scenario could change. Facebook’s Artificial Intelligence Research (FAIR) group and the New York University School of Medicine recently announced a collaborative research project that will use machine-learning techniques to make MRI scans up to 10 times faster.
“Using AI, we believe it may be possible to capture less data and therefore image faster, while still preserving or even enhancing the rich information content of MR images,” Dr. Daniel Sodickson, vice chair for research in radiology at NYU School of Medicine, told Forbes.
Facebook researchers will train their “fastMRI” model using an NYU-provided data set of 3 million images of the knee, brain, and liver. The challenge, according to a Facebook blog, is to get the artificial intelligence network to “recognize the underlying structure of the images in order to fill in views omitted from the accelerated scan” and to bridge those gaps without sacrificing accuracy. Missing a few key pixels could mean the difference between a clear scan and one showing an anomaly such as a torn ligament or a tumor.
If the project is successful, the researchers believe the benefits of faster MRIs would extend beyond a more comfortable patient experience.
“You also get increased accessibility in areas with MRI shortages and you can get improved image quality when you’re trying to image things that move fast, like the heart,” Sodickson told Forbes. “If we can get it fast enough to replace X-rays or CT [scans] then we can also reduce radiation exposure for the population while still getting the critical medical information.”