NIH Director’s Blog features a UNC-led study on how a well-trained computer and its artificial intelligence can help identify potential Long COVID patients. The study, led by UNC School of Medicine’s Emily Pfaff, PhD, was published in the journal The Lancet Digital Health.
The COVID-19 pandemic continues to present considerable public health challenges in the United States and around the globe. One of the most puzzling is why many people who get over an initial and often relatively mild COVID illness later develop new and potentially debilitating symptoms. These symptoms run the gamut including fatigue, shortness of breath, brain fog, anxiety, and gastrointestinal trouble.
People understandably want answers to help them manage this complex condition referred to as Long COVID syndrome. But because Long COVID is so variable from person to person, it’s extremely difficult to work backwards and determine what these people had in common that might have made them susceptible to Long COVID. The variability also makes it difficult to identify all those who have Long COVID, whether they realize it or not. But a recent study, published in the journal Lancet Digital Health led by UNC School of Medicine’s Emily R. Pfaff, PhD, shows that a well-trained computer and its artificial intelligence can help.
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