**2.8 Sepsis deterioration index**

A sepsis deterioration index is a numerical value predicting the chance of a patient becoming septic by a predictive model. This model usually has pre-specified input variables that have a high likelihood of predicting the output variable of sepsis. With the proliferation of healthcare data in the last two decades due to the mandated use of electronic health records, we are now approaching an era where there is enough data to train machine learning models to predict sepsis. The electronic health record (EHR) system Epic is estimated to have approximately 30,000 data points per patient [19]. While large volumes of data are now becoming available, the data must be formatted in a way that can be processed by machine learning models. Healthcare data within EHR repositories tends to be heterogenous and require extensive cleansing before becoming usable for this purpose. Clinical data is rarely standardized and is entered into the EHR without the intention of being utilized for back-end data analysis. Prior studies of de-identified Epic-derived data have characterized these issues and encouraged standardized data entry by clinical staff on the front-end [20].

This is a lofty goal which may be attained at some point in the future. For now, data can be entered into machine learning models through feature extraction followed by creative cleansing and wrangling methods to be discussed. We will first describe in detail the derivation of our institution's sepsis deterioration index. Afterwards we will discuss how our model was trained and compare it to existing models.
