Researchers at Mount Sinai in New York see promise in new machine learning models they’ve developed that can assess – within key windows of time – the risk of certain adverse clinical events in some COVID-19 patients.
WHY IT MATTERS
Research published earlier this month in the Journal of Medical Internet Research describes how the algorithms are enabling better insights into potential risks for a diverse group of COVID-19 patients.
Researchers at Mount Sinai’s Icahn School of Medicine and Hasso Plattner Institute for Digital Health gathered electronic health record data from more than 4,000 adult patients admitted to five Mount Sinai Health System hospitals from this spring, during the pandemic’s first wave.
Clinicians from the Mount Sinai Covid Informatics Center analyzed characteristics of COVID-19 patients – looking at past medical history, comorbidities, vitals and labs – to help predict the risk of mortality, or critical events such as the need for intubation, within clinically relevant time windows.
By predicting risks for time windows of three, five, seven and 10 days from admission, Mount Sinai researchers say the models offer valuable insights to forecast short and medium-term care