Enhancing Early Mortality Prediction for Sepsis-associated ARDS Patients Using Machine Learning
journals.lww.comThis study utilized the MIMIC-IV, eICU CRD, and NWICU databases to construct and validate a machine learning model, SAFE-Mo, which predicts early mortality in patients with sepsis-associated acute respiratory distress syndrome (ARDS) and outperforms traditional prediction models across all metrics.
SAFE-Mo can guide clinicians to focus on critical indicators such as lactate, urine output, anion gap, and others, enabling appropriate measures to improve clinical outcomes for high-risk patients.
Data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.0), eICU Collaborative Research Database (eICU CRD, v2.0), and Northwest ICU (NWICU, v0.1.0) using Structured Query Language.
SAFE-Mo was constructed using machine learning algorithm (svmRadialSigma) focusing on median survival days among deceased patients as the primary outcome.
