Machine learning model from the largest US COVID-19 dataset predicts disease severity

THE VERGE, 13/07/2021

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Beesens TEAM

Machine learning model from the largest US COVID-19 dataset predicts disease severity

"A centralized repository of COVID-19 health records built last year is beginning to show results, starting with a new paper published today. The repository is the largest set of COVID-19 records to date, and was built by a team of researchers and data experts last year to help make sense of COVID-19.

The study, published in the journal JAMA Network Open, looked at risk factors for severe cases of COVID-19 and traced the progression of the disease over time. The authors built machine learning models to predict which hospitalized patients would develop severe disease based on information collected on their first day in a hospital.

Using the centralized database, called the National COVID Cohort Collaborative Data Enclave, or N3C, meant the research team was able to include hundreds of thousands of patients’ records in its analysis. The study used data from 34 medical centers and included over 1 million adults — 174,568 who tested positive for COVID-19 and 1,133,848 who tested negative. It includes records stretching from January 2020 to December 2020.

The analysis shows how treatment for COVID-19 changed over the course of 2020, as doctors tried new treatments and gained more experience with the condition. The percentage of patients who were treated with the anti-malaria drug hydroxychloroquine, which was promoted by former President Donald Trump before proving to be ineffective, dropped off to nearly zero by May 2020. Use of the steroid dexamethasone ticked up in June, after studies showed it could improve survival rates..." Lire la suite