The aim is to develop a machine learning model to predict 28-day mortality in patients with alcoholic cirrhosis and sepsis.
Utilized the MIMIC-IV database for data extraction and analysis.
Employed the XGBoost machine learning algorithm for prediction.
Evaluated model performance based on predictive accuracy and robustness.
The XGBoost model demonstrated superior predictive performance compared to other models.
This model can assist in guiding personalized therapy and resource allocation in critical care.
Abstract
The XGBoost model had the best predictive performance, providinga robust tool to guide personalized therapy and optimize critical care resource allocation.
A machine learning model for predicting 28-day mortality in patients with alcoholic cirrhosis and sepsis: a study based on the MIMIC-IV database. | Synapse