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 the performance of integrated models and minimize systemic bias. We developed a prediction model of sympt...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 16532 - 17
Hlavní autoři: Li, Jiang, Chaudhary, Durgesh, Sharma, Vaibhav, Sharma, Vishakha, Avula, Venkatesh, Ssentongo, Paddy, Wolk, Donna M., Zand, Ramin, Abedi, Vida
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 02.10.2023
Nature Publishing Group
Nature Portfolio
Témata:
631/114
631/208
692/499
Algorithms
Clostridioides difficile
Electronic health records
Electronic medical records
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Performance evaluation
Phenotyping
Prediction models
Risk factors
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Shrnutí: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 the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile  infection(CDI) by integrating common EHR-based 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 performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-41753-7