High-Fidelity Synthetic Data Applications for Data Augmentation

Abstract

The use of high-fidelity synthetic data for data augmentation is an area of growing interest in data science. In this chapter, the concept of synthetic data is introduced, and different types of synthetic data are discussed in terms of their utility or fidelity. Approaches to synthetic data generation are presented and compared with computer modelling and simulation approaches, highlighting the unique benefits of high-fidelity synthetic data. One of the main applications of high-fidelity synthetic data is supporting the training and validation of machine learning algorithms, where it can provide a virtually unlimited amount of diverse and high-quality data to improve the accuracy and robustness of models. Furthermore, high-fidelity synthetic data can address missing data and biases due to under-sampling using techniques such as BayesBoost, as well as boost sample sizes in scenarios where the real data is based on a small sample. Another important application is generating virtual patient cohorts, such as digital twins, to estimate counterfactuals in silico trials, allowing for better prediction of treatment outcomes and personalised medicine. The chapter concludes by identifying areas for further research in the field, including developing more efficient and accurate synthetic data generation methods and exploring the ethical implications of using synthetic data.