Goto

Collaborating Authors

Facebook data could predict spread of disease outbreaks says new research on 'social-connectedness'

Daily Mail - Science & tech

Researchers say evaluating the'social-connectedness' of regions using Facebook data could give epidemiologists another tool in judging the spread of infectious disease outside of geographic proximity and population density. The study, which appears in the preprint journal ArXiv and is authored by researchers from New York University, found links between two hotspots of the ongoing COVID-19 pandemic - Westchester County, New York and Lodi province in Italy - to areas with correlating connections on the social media platform, Facebook. Using an equation developed by the same researchers in 2017 called the'Social Connectedness Index' the study was able to make correlations between the spread of COVID-19 from Westchester County and Lodi to geographically disparate locations like ski resorts on Florida and vacation spots in Rimini, Italy near the Adriatic sea. Those correlations remained even after controlling for wealth, population density, and geographic proximity according to researchers. Levels of social connectedness didn't always correlate to the disproportionate spread of the virus, however.


LI artificial intelligence startup predicts where COVID-19 will spike – IAM Network

#artificialintelligence

A Long Island artificial intelligence startup has built software aimed at pinpointing U.S. counties where the COVID-19 outbreak is likely to be most deadly. In a June report, the data-mining company, Akai Kaeru LLC, forecast spiking COVID-19 mortality with the heaviest concentrations in counties of the Southeast, including Mississippi, Georgia and Louisiana, said co-founder and chief executive Klaus Mueller. Nationwide, the software found 985 out of all 3,007 U.S. counties are at risk. "These patterns identify groups of counties that have a steeper increase in the death-rate trajectory," he said. Closer to home, the software found Nassau and Suffolk counties are likely to be relatively stable, but Westchester and Rockland counties are potential tinderboxes that could tip into crisis, said Mueller, a computer science professor on leave from Stony Brook University.


A.I. Versus M.D.

#artificialintelligence

One evening last November, a fifty-four-year-old woman from the Bronx arrived at the emergency room at Columbia University's medical center with a grinding headache. Her vision had become blurry, she told the E.R. doctors, and her left hand felt numb and weak. The doctors examined her and ordered a CT scan of her head. A few months later, on a morning this January, a team of four radiologists-in-training huddled in front of a computer in a third-floor room of the hospital. The room was windowless and dark, aside from the light from the screen, which looked as if it had been filtered through seawater. The residents filled a cubicle, and Angela Lignelli-Dipple, the chief of neuroradiology at Columbia, stood behind them with a pencil and pad. She was training them to read CT scans. "It's easy to diagnose a stroke once the brain is dead and gray," she said. "The trick is to diagnose the stroke before too many nerve cells begin to die." Strokes are usually caused by blockages or bleeds, and a neuroradiologist has about a forty-five-minute window to make a diagnosis, so that doctors might be able to intervene--to dissolve a growing clot, say. "Imagine you are in the E.R.," Lignelli-Dipple continued, raising the ante. "Every minute that passes, some part of the brain is dying. Time lost is brain lost." She glanced at a clock on the wall, as the seconds ticked by. "So where's the problem?" she asked. The blood supply to the brain branches left and right and then breaks into rivulets and tributaries on each side. A clot or a bleed usually affects only one of these branches, leading to a one-sided deficit in a part of the brain. As the nerve cells lose their blood supply and die, the tissue swells subtly.


Google Secretly Tests Medical Records Search Tool On Nation's Largest Nonprofit Health System, Documents Show

#artificialintelligence

David Feinberg, Google's Vice President of Healthcare, recently described "a search bar on top of ... [ ] your [electronic health records] that needs no training," on stage at a conference in Las Vegas. Google is testing a service that would use its search and artificial intelligence technology to analyze patient records for Ascension, the largest nonprofit health system in the U.S., according to documents about the efforts reviewed by Forbes. Called "'Nightingale," the Google-Ascension project indicates that Google's push into health analysis is farther along than previously believed, even as the company has faced a growing backlash over health-related privacy concerns. Ascension said in a statement that all its work with Google complies with privacy law and is "underpinned by a robust data security and protection effort, which Google echoed in its own blog post later Monday, including that "patient data cannot and will not be combined with any Google consumer data. " The Wall Street Journal first published details of the Ascension partnership earlier on Monday.


Merck turning to machine learning to prevent drug shortages: Merck plans to use a cloud-based software platform to better predict and prevent drug shortages, according to The Wall Street Journal.

#artificialintelligence

Merck plans to use a cloud-based software platform to better predict and prevent drug shortages, according to The Wall Street Journal. The platform, developed by healthcare software company TraceLink, will analyze in real time data from pharmacies, hospitals and wholesale distributors. By using analytics and machine learning, the software can improve predictions and help drugmakers better match drug demand. The software could also save drugmakers hundreds of millions of dollars annually by reducing waste and avoiding costs like expedited shipments, because it can track a drug's status at every step in the supply chain. The platform currently holds data on more than 6 billion drugs.