Researchers propose a machine learning model for early prediction of adverse events in hospitalized patients using retrospectively collected deterioration index scores. The model's performance is compared with currently deployed early warning systems.
In a recent article published in eClinicalMedicine, researchers propose a novel predictive model based on machine learning (ML) for the early prediction of adverse events (AEs), such as cardiac arrest and death, in hospitalized patients using retrospectively collected deterioration index (DI) scores.
The performance of this tool was compared with the currently deployed proprietary early warning systems (EWSs) utilizing the DI exceeding 60 hypothesis used to predict a composite AE that includes cardiac arrest, all-cause mortality, and need for an intensive care unit (ICU) admission
Machine Learning Predictive Model Adverse Events Early Prediction Hospitalized Patients Deterioration Index Scores Early Warning Systems
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