title: Designing and testing computer based screening engine for severe sepsis/septic shock.
abstract: This study addresses the role of a sepsis "sniffer", an automatic screening tool for the timely identification of patients with severe sepsis/septic shock, based electronic medical records. During the two months prospective implementation in a medical intensive care unit, 37 of 320 consecutive patients developed severe sepsis/septic shock. The sniffer demonstrated a sensitivity of 48% and specificity of 86%, and positive predictive value 32%. Further improvements are needed prior to the implementation of sepsis sniffer in clinical practice and research.
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: V
authors.initials: V
authors.lastname: Herasevich
authors.forename: B
authors.initials: B
authors.lastname: Afessa
authors.forename: C G
authors.initials: CG
authors.lastname: Chute
authors.forename: O
authors.initials: O
authors.lastname: Gajic
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003422:Critical Care / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008403:Mass Screening / Q000379:methods; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D008910:Minnesota; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000145:classification* / Q000175:diagnosis*; D012984:Software; D012985:Software Design; D009626:Terminology as Topic