Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction
and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study,
demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York,
with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records
and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a
transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory
measurements given the entire patients’ hospital stay. The study population includes 3699 COVID-19 positive (57%
male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver
operator curve (0.92) for next-day mortality prediction given entire patients’ trajectories, and through masking, it
learnt each variable’s context