Related Experiment Video
Updated: Sep 9, 2025
Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291
Published on: December 10, 2016
Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and
1Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Medical School, 1150 W. Medical Center Dr, 1510B MSRB1, Ann Arbor, MI 48103 USA.
Abstract:
Despite 2 decades of effort, there is a lack of clinically deployed models for predicting incident, severe, or recurrent Clostridioides difficile infection (CDI). This review outlines the promise of machine learning and biomarker-augmented models for targeted prevention and treatment, but also emphasizes the challenges of real-world deployment-namely integration into clinical workflows and governance. Moving forward, progress will depend on translational biomarker development, pragmatic modeling pipelines, and continuous monitoring. With these elements in place, CDI prediction tools can become a template for precision prevention of healthcare-associated infections.
Related Concept Videos
01:18Steps in Outbreak Investigation
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.