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Irvine, Calif., Feb. 9, 2021 Monoclonal antibodies are showing promise for improving outcomes for COVID-19 patients, but when a hospital is already beyond capacity, administering them can be a challenge. As hospitalizations soared across California, clinicians with UCI Health created a system for delivering monoclonal antibodies that is keeping hospital beds available for patients with the greatest need. The hospital bed is one of the most valuable resources that we have, which has been stretched thin by the COVID-19 pandemic, said Dr. Daniel S. Chow, an assistant professor in residence in radiological sciences and co-director for the Center for Artificial Intelligence in Diagnostic Medicine as well as the project s co-principal investigator. Every effort to expand the number of beds available counts, and that includes being proactive about preventing hospitalizations.
AI, Machine Learning Tools Help Predict COVID-19 Outcomes healthitanalytics.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from healthitanalytics.com Daily Mail and Mail on Sunday newspapers.
A machine-learning model created to calculate COVID-19 health outcomes
University of California, Irvine health sciences researchers have created a machine-learning model to predict the probability that a COVID-19 patient will need a ventilator or ICU care. The tool is free and available online for any healthcare organization to use. The goal is to give an earlier alert to clinicians to identify patients who may be vulnerable at the onset, said Daniel S. Chow, an assistant professor in residence in radiological sciences and first author of the study, published in
PLOS ONE. The tool predicts whether a patient s condition will worsen within 72 hours.
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Irvine, Calif., Dec. 17, 2020 University of California, Irvine health sciences researchers have created a machine-learning model to predict the probability that a COVID-19 patient will need a ventilator or ICU care. The tool is free and available online for any healthcare organization to use. The goal is to give an earlier alert to clinicians to identify patients who may be vulnerable at the onset, said Daniel S. Chow, an assistant professor in residence in radiological sciences and first author of the study, published in
PLOS ONE. The tool predicts whether a patient s condition will worsen within 72 hours.
Coupled with decision-making specific to the healthcare setting in which the tool is used, the model uses a patient s medical history to determine who can be sent home and who will need critical care. The study found that at UCI Health, the tool s predictions were accurate about 95 percent of the time.
Researchers create model to calculate COVID-19 health outcomes medicalxpress.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from medicalxpress.com Daily Mail and Mail on Sunday newspapers.