**3. Methodology: creating a sepsis deterioration machine learning model**

The dataset to create our Cedars- Sinai Deterioration Index (CS-DI) consisted of 1521 hospital admitted patients from June 1st, 2021– September 1st, 2021, and is a representation of a standard medical/surgical unit patient population, containing 157,845 encounters. We used 70% (110,492) encounters for training, and 30% (47,353) encounters for testing. The average age of patients in the dataset is 63.22 years. 95,844 of patients identified as male, 61,203 of patients identified as female, and 798 of patients identified as other. 89,517 patients identified as Caucasian, 13,430 patients identified as Asian, 23,568 patients identified as Black or African American, 401 patients identified as American Indian or Alaska Native, and 29,624 patients identified as Other/Unknown. The dataset includes lab results, nursing assessments, vital signs, and a predictor for an event, which is a binary indicator for an escalation of care, classified as a transfer to an Intensive Care Unit (ICU), Respiratory or Cardiac Arrest (Code Blue), or Death (Mortality).
