An Ensemble Machine Learning model for the early detection of sepsis from Clinical Data Mengsha F Menglin L Zhenhua X Aims: The goal is the early detection of sepsis using physiological data. The study aimed to predict sepsis 6 hours before the clinical prediction of sepsis, and there is a utility function that rewards early predictions and penalizes late predictions as well as false alarms. Method: First analyzed and preprocessed the data, included: excluding outliers and filling missing values with the mean value. Patients were excluded if they developed sepsis with the first record and age < 18 years; Features were chosen from 40 columns, Variables with too large missing values and low correlation are excluded. An Integrated model was designed to train and predict. Result：5000 files from training_setB were used to train, where included 279 patients,188453 records, 2623 sepsis records; The test data were randomly chosen from training_setA:2000 files, 184 patients,1760 sepsis records; The utility score is 0.81 in the local test files.Additional evaluation indicators (AUROC=0.74,AUPRC=0.77). Conclusion: An Ensemble model can predict sepsis 6 hours prior to its onset. Future studies will validate in a large cohort.