A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains.

Computers in Biology and Medicine
Vasileios C PezoulasDimitrios I Fotiadis

Abstract

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical research databases with significant lack of population size. However, the impact of data augmentation on the development of AI (artificial intelligence) models to address clinical unmet needs has not yet been investigated. In this work, we assess whether the aggregation of real with virtual patient data can improve the performance of the existing risk stratification and disease classification models in two rare clinical domains, namely the primary Sjögren's Syndrome (pSS) and the hypertrophic cardiomyopathy (HCM), for the first time in the literature. To do so, multivariate approaches, such as, the multivariate normal distribution (MVND), and straightforward ones, such as, the Bayesian networks, the artificial neural networks (ANNs), and the tree ensembles are compared against their performance towards the generation of high-quality virtual data. Both boosting and bagging algorithms, such as, the Gradient boosting trees (XGBoost), the AdaBoost and the Random Forests (RFs) were trained on the augmented data to evaluate the performance improvement for lymphoma classification and HCM risk...Continue Reading

References

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Jun 8, 2018·Genetics in Medicine : Official Journal of the American College of Medical Genetics·Francesco MazzarottoIacopo Olivotto
Mar 18, 2019·Computers in Biology and Medicine·Vasileios C PezoulasDimitrios I Fotiadis
Jan 18, 2020·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Vasileios C PezoulasDimitrios I Fotiadis
Oct 7, 2020·Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Vasileios C PezoulasDimitrios I Fotiadis

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