Project Date: 
May 2014

Sepsis is a severe medical condition with high hospitalization cost and high mortality rates, with more than half of the patients with septic shock dying within 30 days. While scoring tests and alert systems are in place for sepsis patients, researchers currently lack a data-based tool to guide clinicians on early prognosis and effective treatment of sepsis.


Researchers propose to create an adaptive, data-driven, probabilistic clinician decision support system (CDSS) that will employ state of the art machine learning tools to mine the data rich Electronic Medical Records of the UC Davis Health System and can be readily deployed throughout hospitals in CA and around the country. Specifically, the project will be organized in three main phases: data mining, system design and implementation, assessment and refinement.