Researchers at the Perelman School of Medicine at the University of Pennsylvania have developed a machine learning technique that could improve care for hospital patients with long COVID-19 by analyzing electronic health records to identify specific patient sub-populations and their needs. Led by Yong Chen, a professor of Biostatistics, and Qiong Wu, a former post-doctoral researcher, the study used a technique called latent transfer learning to examine data from eight pediatric hospitals. The system identified four distinct sub-populations of patients with pre-existing health conditions, including mental health conditions, atopic or allergic chronic conditions, non-complex chronic conditions, and complex chronic conditions. This information could help hospitals allocate resources more effectively and provide targeted care to high-risk patients. The study's findings have implications for managing not only long COVID-19 but also other common chronic conditions such as diabetes, heart disease, and asthma.
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