AI Article Synopsis
- Incorporating repeated vital and lab measurements can enhance mortality risk prediction and help pinpoint critical risk factors for COVID-19 patients in hospitals.
- An observational study analyzed data from 3,699 COVID-19 patients admitted to five Mount Sinai Health System hospitals between March and June 2020, comparing survivors to non-survivors.
- The study used a sophisticated model called BEHRTDAY, which achieved high accuracy (precision score of 0.96 and area under the curve of 0.92) in predicting next-day mortality by evaluating the full history of patient vitals and lab results.
Article Abstract
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.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285184 | PMC |
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