Researchers explain how online symptom checkers generate probable diagnoses and suggestions news-medical.net - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from news-medical.net Daily Mail and Mail on Sunday newspapers.
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UNIVERSITY PARK, Pa. Have you recently turned to your mobile device or computer to find out if your cough, sniffle or fever could be caused by COVID-19?
The online symptom checker you used may have advised you to stay home and call your medical provider if symptoms worsen, or perhaps told you that you may be eligible for COVID-19 testing. But why did it make the recommendation it did? And how should you know if you can trust it?
Those are questions that researchers at the Penn State College of Information Sciences and Technology recently explored through a project in which they augmented online symptom checkers by offering explanations of how the system generated its probable diagnoses and suggestions while also studying users perceptions of those recommendations.
New tool could help lessen bias in live television broadcasts psu.edu - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from psu.edu Daily Mail and Mail on Sunday newspapers.
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“This gift is a testament to the Lees’ experience at Penn State, specifically to Corey’s at the College of IST and the impact that experience had on him,” said Andrew Sears, dean of the College of IST. “We are grateful that they, as alumni, are honoring their experience in an impactful way that will support a new generation of IST students.”
The scholarship was matched dollar-for-dollar by the College of Information Sciences and Technology Dean’s Advisory Board and supports IST students with financial need who are first-generation or from underrepresented backgrounds.
“Scholarship support is the college’s highest philanthropic priority and we are grateful for Corey and Leteace’s commitment to supporting our current and future students,” said Mike Weyandt, director of development at the College of IST. “It will not only allow us to support a diverse group of students, it will help lessen the financial burden that many of our students endure.
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UNIVERSITY PARK, Pa. Did you ever wonder how social networking applications like Facebook and LinkedIn make recommendations on the people you should friend or pages you should follow?
Behind the scenes are machine learning models that classify nodes based on the data they contain about users for example, their level of education, location or political affiliation. The models then use these classifications to recommend people and pages to each user. But there is significant bias in the recommendations made by these models known as graph neural networks (GNNs) as they rely on user features that are highly related to sensitive attributes such as gender or skin color.