Evaluating the Impact of Health Care Data Completeness for Deep Generative Models

Author:

Smith Benjamin1,Van Steelandt Senne2,Khojandi Anahita3

Affiliation:

1. Bredesen Center, University of Tennessee, Knoxville, Tennessee, United States

2. Department of Business Analytics and Statistics, University of Tennessee, Knoxville, Tennessee, United States

3. Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, Tennessee, United States

Abstract

Abstract Background Deep generative models (DGMs) present a promising avenue for generating realistic, synthetic data to augment existing health care datasets. However, exactly how the completeness of the original dataset affects the quality of the generated synthetic data is unclear. Objectives In this paper, we investigate the effect of data completeness on samples generated by the most common DGM paradigms. Methods We create both cross-sectional and panel datasets with varying missingness and subset rates and train generative adversarial networks, variational autoencoders, and autoregressive models (Transformers) on these datasets. We then compare the distributions of generated data with original training data to measure similarity. Results We find that increased incompleteness is directly correlated with increased dissimilarity between original and generated samples produced through DGMs. Conclusions Care must be taken when using DGMs to generate synthetic data as data completeness issues can affect the quality of generated data in both panel and cross-sectional datasets.

Publisher

Georg Thieme Verlag KG

Subject

Health Information Management,Advanced and Specialized Nursing,Health Informatics

Reference30 articles.

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