Evaluation of the Synthetic Electronic Health Records

Emily Muller; Xu Zheng; Jer Hayes

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Paper

Evaluation of the Synthetic Electronic Health Records

In Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare SDAIH - Volume 1, 17-22, 2022 , Vienna, Austria

Authors: Emily Muller ¹ ; Xu Zheng ² and Jer Hayes ²

Affiliations: ¹ Department of Epidemiology and Biostatistics, Imperial College London, U.K. ; ² Accenture Labs, Dublin, Ireland

Keyword(s): Generative Model, Medical Data Synthesis, Synthetic Data Evaluation.

Abstract: Generative models have been found effective for data synthesis due to their ability to capture complex underlying data distributions. The quality of generated data from these models is commonly evaluated by visual inspection for image datasets or downstream analytical tasks for tabular datasets. These evaluation methods neither measure the implicit data distribution nor consider the data privacy issues, and it remains an open question of how to compare and rank different generative models. Medical data can be sensitive, so it is of great importance to draw privacy concerns of patients while maintaining the data utility of the synthetic dataset. Beyond the utility evaluation, this work outlines two metrics called Similarity and Uniqueness for sample-wise assessment of synthetic datasets. We demonstrate the proposed notions with several state-of-the-art generative models to synthesise Cystic Fibrosis (CF) patients’ electronic health records (EHRs), observing that the proposed metrics a re suitable for synthetic data evaluation and generative model comparison. (More)

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Paper citation in several formats:

Muller, E.; Zheng, X. and Hayes, J. (2023). Evaluation of the Synthetic Electronic Health Records. In Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - SDAIH; ISBN 978-989-758-629-3, SciTePress, pages 17-22. DOI: 10.5220/0011531300003523

@conference{sdaih23,
author={Emily Muller. and Xu Zheng. and Jer Hayes.},
title={Evaluation of the Synthetic Electronic Health Records},
booktitle={Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - SDAIH},
year={2023},
pages={17-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011531300003523},
isbn={978-989-758-629-3},
}

TY - CONF

JO - Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare - SDAIH
TI - Evaluation of the Synthetic Electronic Health Records
SN - 978-989-758-629-3
AU - Muller, E.
AU - Zheng, X.
AU - Hayes, J.
PY - 2023
SP - 17
EP - 22
DO - 10.5220/0011531300003523
PB - SciTePress