Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence <i>Clostridioides difficile</i> risk, including features clinically proven to increase susceptibility. These human-interpretable models can help distinguish significant predictors that affect a patient's chance of testing positive, which may influence their <i>Clostridioides difficile</i> risk.