Researchers developed and validated a machine learning model based on logistic regression that used routine clinical and treatment data to accurately predict in-hospital deaths among patients with ...
Among hospitalized patients with cirrhosis, a machine learning (ML) model enhanced mortality prediction compared with traditional methods and was consistent across country income levels in a large ...
It would be greatly beneficial to physicians trying to save lives in intensive care units if they could be alerted when a patient's condition rapidly deteriorates or shows vitals in highly abnormal ...
Sepsis is one of the most common and lethal syndromes encountered in intensive care units (ICUs), and acute respiratory failure (ARF) represents one of its most critical complications. Once ...
This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model ...
Researchers developed and externally validated a machine learning model to predict the 28-day mortality risk in ICU patients with sepsis complicated by acute respiratory failure. Using routinely ...