Early detection of sepsis in the emergency department using Dynamic Bayesian Networks.

Sepsis is a systemic inflammatory state due to an infection, and is associated with very high mortality and morbidity. Early diagnosis and prompt antibiotic and supportive therapy is associated with improved outcomes. Our objective was to detect the presence of sepsis soon after the patient visits the emergency department. We used Dynamic Bayesian Networks, a temporal probabilistic technique to model a system whose state changes over time. We built, trained and tested the model using data of 3,100 patients admitted to the emergency department, and measured the accuracy of detecting sepsis using data collected within the first 3 hours, 6 hours, 12 hours and 24 hours after admission. The area under the curve was 0.911, 0.915, 0.937 and 0.944 respectively. We describe the data, data preparation techniques, model, results, various statistical measures and the limitations of our experiments. We also briefly discuss techniques to improve accuracy, and the generalizability of our methods to other diseases.