**6. Conclusion**

Sepsis is a ubiquitous condition across healthcare continuum causing millions of deaths annually and incurring high costs on the healthcare system. We have made great strides in the ability to identify and treat sepsis, but it still kills nearly 270,000 people annually in the U.S. 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. For the purposes of predicting sepsis deterioration, we used regression to determine the association between variables (also known as features) to eventually predict sepsis. Among the cohort examined in our model at Cedars Sinai, we found patients who met or exceeded the set threshold of 68.8 had an 87% probability of deterioration to sepsis during their hospitalization and a median lead time of 24 hours from when the threshold was first exceeded. Another model incorporating unstructured text into their deterioration model, had an AUROC (Area Under Receiver Operator Curve) as high as 0.87 with a lead time of 48 hours before the onset of sepsis. There is no easy way to determine an intervention point of the deterioration predictive model. The author's recommendation is to continually modify this inflection point guided by data from near-misses and mis-categorized patients. Collecting real-time feedback from end-users on alert accuracy is also crucial for a model to survive. An ML deterioration model to predict sepsis produces ample value in a healthcare organization if deployed in conjunction with human intervention and continuous prospective re-assessment.
