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Risk Analysis of Clostridiodides Difficile Infections in a Hospital Setting and the Impact of Prior Choice on Predictive Capability
Blanchard, Trevor D
Blanchard, Trevor D
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Abstract
Healthcare-associated Clostridiodides Difficile (C. diff.) infections are one of the most common healthcare associated infections in the U.S., leading to thousands of deaths per year. Machine learning algorithms have shown some ability to predict who is most vul- nerable to C. diff. infection utilizing electronic health records obtained soon after admittance, but these models have shown insufficient predictive capability. We extracted data from the electronic medical records provided in the MIMIC-III Clinical Database which contains data from the Beth Israel Deaconess Medical Center between 2001 and 2012, resulting in very large predictor matrices. We aimed to predict which patients would receive a positive test for C. diff. using a Bayesian logistic regression model. We examined the impact of three different priors, a normal, double exponential, and regularized horseshoe prior to understand how prior choice influenced predictive capability and the size of coefficients. We used cross-validation to test the predictive capability of each prior, and compared results between models using ROC and PR curves. Our results show that of the three priors, the regularized horseshoe prior achieves the highest prediction accuracy.
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Date
2023-01-01