*Developing and Deploying a Sepsis Deterioration Machine Learning Algorithm DOI: http://dx.doi.org/10.5772/intechopen.111557*

as patient demographics, vitals data and laboratory data. By adding text data into the mix, the AUC of their Sepsis early risk assessment (SERA) model was as high as 0.87 with a lead time of 48 hours before the onset of sepsis.

Unstructured data in these models increases the accuracy and lead time as expected. Healthcare professionals rely on a multitude of unstructured data including all the data above and a physical assessment of the patient. The more of these features we can incorporate into models, the more accurate they can become. Humans cannot be omniscient to continuously monitor all the data they are presented and make real-time assessments on every patient in the hospital. If we can train a machine to think like an astute healthcare professional with the processing power of a supercomputer, we can ideally reduce the incidence of sepsis in our healthcare systems before it occurs.
