Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

ABSTRACT: BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between interventions. OBJECTIVE We sought to comprehensively estimate changes in length of stay (LOS) attributable to CDI at a single urban tertiary-care facility using only data automatically extractable from the electronic medical record (EMR). METHODS We performed a retrospective cohort study of 171,938 visits spanning a 7-year period. In total, 23,968 variables were extracted from EMR data recorded within 24 hours of admission to train elastic-net regularized logistic regression models for propensity score matching. To address time-dependent bias (r

SUBMITTER: Pak TR 

PROVIDER: S-EPMC5923033 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature