title: Learning a Severity Score for Sepsis: A Novel Approach based on Clinical Comparisons.
abstract: Sepsis is one of the leading causes of death in the United States. Early administration of treatment has been shown to decrease sepsis-related mortality and morbidity. Existing scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE II) and Sequential Organ Failure Assessment scores (SOFA) achieve poor sensitivity in distinguishing between the different stages of sepsis. Recently, we proposed the Disease Severity Score Learning (DSSL) framework that automatically derives a severity score from data based on clinical comparisons - pairs of disease states ordered by their severity. In this paper, we test the feasibility of using DSSL to develop a sepsis severity score. We show that the learned score significantly outperforms APACHE-II and SOFA in distinguishing between the different stages of sepsis. Additionally, the learned score is sensitive to changes in severity leading up to septic shock and post treatment administration.
authors.affiliation: Dept. of Computer Science, Johns Hopkins University, Baltimore, MD.
authors.forename: Kirill
authors.initials: K
authors.lastname: Dyagilev
authors.affiliation: Dept. of Computer Science, Johns Hopkins University, Baltimore, MD; Dept. of Health Policy & Mgmt., Johns Hopkins University, Baltimore, MD.
authors.forename: Suchi
authors.initials: S
authors.lastname: Saria
mesh_terms: D018806:APACHE; D001330:Electronic Data Processing; D006801:Humans; D000069550:Machine Learning; D011379:Prognosis; D018805:Sepsis / Q000473:pathology*; D012772:Shock, Septic