OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.
OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
OBJECTIVE: To evaluate the effect of the early goal-directed therapy (EGDT) on mortality in patients with septic shock, and to analyze the risk factors of mortality.
OBJECTIVE: To investigate whether esmolol could improve clinical outcome and tissue oxygen metabolism by controlling heart rate (HR) in patients with septic shock.
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