Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

Published in: BMC medical informatics and decision making (2020)

The machine learning-based models developed in this study had good prediction performance. Amongst them, the GBM model showed the best performance in predicting the risk of in-hospital death. It has the potential to assist physicians in the ICU to perform appropriate clinical interventions for critically ill sepsis patients and thus may help improve the prognoses of sepsis patients in the ICU.

Keyphrases

intensive care unit
end stage renal disease
machine learning
ejection fraction
newly diagnosed
chronic kidney disease
prognostic factors
acute kidney injury
primary care
physical activity
patient reported outcomes
artificial intelligence
septic shock
patient reported
adverse drug