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Artificial Intelligence Can Help Doctors Manage COVID-19

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Chest x-rays used in the COVID-Net study show differing infection extent and opacity in the lungs of COVID-19 patients. Artificial intelligence (AI) technology developed by researchers at the University of Waterloo is capable of assessing the severity of COVID-19 cases with a promising degree of accuracy. A study, which is part of the COVID-Net open-source initiative launched more than a year ago, involved researchers from Waterloo and spin-off start-up company DarwinAI, as well as radiologists at the Stony Brook School of Medicine and the Montefiore Medical Center in New York. Deep-learning AI was trained to analyze the extent and opacity of infection in the lungs of COVID-19 patients based on chest x-rays. Its scores were then compared to assessments of the same x-rays by expert radiologists.


Machine Learning Can Identify Areas Most at Risk from Pandemic

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Areas most at risk from the COVID-19 pandemic can be identified by a new machine learning tool developed by researchers at startup company Akai Kaeru LLC, which is affiliated with Stony Brook University's Department of Computer Science and the Institute for Advanced Computational Science. The software they use analyzes a massive data set from all 3,007 U.S. counties. The researchers found that combinations of factors such as poverty, rural settings, low education, low poverty but housing debt, and sleep deprivation are associated with higher death rates in counties. The researchers use an automatic pattern mining engine and software to analyze a data set with approximately 500 attributes, which cover details related to demographics, economics, race and ethnicity, and infrastructure in all U.S. counties. After analyzing and assessing the data within counties they created nearly 300 sets of counties at a high risk for COVID-19 and related death rates. Many of these counties within the


New machine learning tool identifies US counties at higher risk for COVID deaths

#artificialintelligence

The task of controlling the COVID-19 pandemic nationwide and predicting where cases will spike next and which areas may have high mortality rates remains daunting for scientists and public officials. A new machine learning tool developed by researchers at a startup company (Akai Kaeru LLC) affiliated with Stony Brook University's Department of Computer Science and the Institute for Advanced Computational Science (IACS) may help gauge areas most at risk for the virus and high death rates. The software they use analyzes a massive data set from all 3,007 U.S. counties. They found that combinations of factors such as poverty, rural settings, low education, low poverty but housing debt, and sleep deprivation are associated with higher death rates in counties. The researchers use an automatic pattern mining engine and software to analyze a data set with approximately 500 attributes, which cover details related to demographics, economics, race and ethnicity, and infrastructure in all U.S. counties.