@inproceedings{
kuo2023synthetic,
title={Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models},
author={Nicholas I-Hsien Kuo and Federico Garcia and Anders Sonnerborg and Michael Bohm and Rolf Kaiser and Maurizio Zazzi and Louisa Jorm and Sebastiano Barbieri},
booktitle={NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI},
year={2023},
url={https://openreview.net/forum?id=1MV49Ug6q9}
}
This paper introduces a novel method for simulating Electronic Health Records (EHRs) using Diffusion Probabilistic Models (DPMs). We showcase the ability of DPMs to generate longitudinal EHRs with mixed-type variables – numeric, binary, and categorical. Our approach is benchmarked against existing Generative Adversarial Network (GAN)-based methods in two clinical scenarios: management of acute hypotension in the intensive care unit and antiretroviral therapy for people with human immunodeficiency virus. Our DPM-simulated datasets not only minimise patient disclosure risk but also outperform GAN-generated datasets in terms of realism. These datasets also prove effective for training downstream machine learning algorithms, including reinforcement learning and Cox proportional hazards models for survival analysis.
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
Nicholas I-Hsien Kuo Federico Garcia Anders Sonnerborg Michael Bohm Rolf Kaiser Maurizio Zazzi Louisa Jorm Sebastiano Barbieri