An Integrated Pipeline For Prediction of Clostridioides Difficile Infection

With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based risk factors and genetic risk factors(rs2227306/IL8). Our pipeline includes 1)leveraging phenotyping algorithm to minimize temporal bias, 2)performing simulation studies to determine the predictive power in samples without genetic information, 3)propensity score matching to control for the confoundings, 4)selecting machine learning algorithms to capture complex feature interactions, 5)performing oversampling to address data imbalance, and 6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performace of including common clinical risk factors in prediction of CDI and the benefit of including genetic feature(s) into the prediction models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and the value of genetic feature should be thoroughly evaluated in general population and subgroups.