Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model

Conclusions: Machine-learning methods can be used to predict avoidable blood tests accurately for serum potassium in critically ill pediatric patients. A median of 27.2% of samples could have been saved, with decreased costs and risk of infection or anemia.
Source: Pediatric Critical Care Medicine - Category: Pediatrics Tags: Feature Cardiac Intensive Care Source Type: research