Generation and evaluation of synthetic patient data
By André R. Gonçalves, P. Ray, Braden C. Soper, Jennifer L. Stevens, Linda Coyle, A. Sales. BMC Medical Research Methodology, 2020
385 citations recorded by the source corpus.
Background Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/7561f28a9d063c3275a3b416fe09da1d645e5ede
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Citations in the corpus
- Synthetic data generation for tabular health records: A systematic review (2022, methodology): “Goncalves 2020 [58] Preserve privacy Categorical SEER program (a)Breast cancer (b)Respiratory cancer (c)Non-solid cancer MPoM CLGP MC-medGAN MICE-DT (R)KL divergence (R)PCD (U)Log-cluster (U) Support coverage (U)Cross-classification (P)Identity disclosure (P)Attribute disclosure”
- Diffusion models in medical imaging: A comprehensive survey (2022, background): “[44] have evaluated different generative methods for creating synthetic electronic health records and found that some have the potential to be useful in practice as they produce synthetic samples that have similar statistical properties to real data without compromising patient privacy.”
- Machine Learning for Synthetic Data Generation: a Review (2023, background): “Research in medicine has been greatly facilitated by the availability of information from electronic health records [89], [90].”