Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study.

ABSTRACT: Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical dat

SUBMITTER: Ratzinger F 

PROVIDER: S-EPMC6093921 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature