Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile.

ABSTRACT:

Background

Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile.

Methods

We consider electronic medical record (EMR) data from patients admitted for ≥24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables

SUBMITTER: Wiens J 

PROVIDER: S-EPMC4281796 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature