# Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results

arXiv: https://arxiv.org/abs/2305.07685 · HTML: https://mlsanity.com/paper/2305.07685/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 13 of 13)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Handling Incomplete Heterogeneous Data using VAEs](https://mlsanity.com/paper/1807.03653/) (2018)
- [On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract means](https://mlsanity.com/paper/1904.04017/) (2019)
- [Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications](https://mlsanity.com/paper/2008.02793/) (2020)
- [Adversarial Generation of Natural Language](https://mlsanity.com/paper/1705.10929/) (2017)
- [Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains](https://mlsanity.com/paper/1302.4957/) (2013)
- [Generating Natural Language Adversarial Examples on a Large Scale with Generative Models](https://mlsanity.com/paper/2003.10388/) (2020)
- [Attacks on Deidentification's Defenses](https://mlsanity.com/paper/2202.13470/) (2022)
- [Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?](https://mlsanity.com/paper/2005.13510/) (2020)

## Built on by (most cited first, 5 of 5)

- [HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management](https://mlsanity.com/paper/2409.16395/) (2024)
- [Methods for generating and evaluating synthetic longitudinal patient data: a systematic review](https://mlsanity.com/paper/2309.12380/) (2023)
- [NFDI4Health workflow and service for synthetic data generation, assessment and risk management](https://mlsanity.com/paper/2408.04478/) (2024)
- [Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests](https://mlsanity.com/paper/2508.14936/) (2025)
- [Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy](https://mlsanity.com/paper/2604.07557/) (2026)

## Abstract (arXiv)

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.