Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives.

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Objectives: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset.

Serial Daily Organ Failure Assessment Beyond ICU Day 5 Does Not Independently Add Precision to ICU Risk-of-Death Prediction.

Objectives: To identify circumstances in which repeated measures of organ failure would improve mortality prediction in ICU patients.

Design: Retrospective cohort study, with external validation in a deidentified ICU database.

Setting: Eleven ICUs in three university hospitals within an academic healthcare system in 2014.