# Methods for generating and evaluating synthetic longitudinal patient data: a systematic review

arXiv: https://arxiv.org/abs/2309.12380 · HTML: https://mlsanity.com/paper/2309.12380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 180 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [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)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation](https://mlsanity.com/paper/2002.11603/) (2020)
- [Tabular Transformers for Modeling Multivariate Time Series](https://mlsanity.com/paper/2011.01843/) (2020)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Membership Inference Attacks against Synthetic Data through Overfitting Detection](https://mlsanity.com/paper/2302.12580/) (2023)
- [Differentially Private Synthetic Medical Data Generation using Convolutional GANs](https://mlsanity.com/paper/2012.11774/) (2020)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models](https://mlsanity.com/paper/2208.01230/) (2022)
- [CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records](https://mlsanity.com/paper/2001.09346/) (2020)
- [PPGAN: Privacy-preserving Generative Adversarial Network](https://mlsanity.com/paper/1910.02007/) (2019)
- [A Hybrid Convolutional Variational Autoencoder for Text Generation](https://mlsanity.com/paper/1702.02390/) (2017)
- [T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling](https://mlsanity.com/paper/1811.08295/) (2018)
- [EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders](https://mlsanity.com/paper/2012.10020/) (2020)
- [A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data](https://mlsanity.com/paper/2301.10053/) (2023)
- [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)

## Built on by (most cited first, 1 of 1)

- [Evaluation metrics for temporal preservation in synthetic longitudinal patient data](https://mlsanity.com/paper/2602.10643/) (2026)

## Abstract (arXiv)

The rapid growth in data availability has facilitated research and development, yet not all industries have benefited equally due to legal and privacy constraints. The healthcare sector faces significant challenges in utilizing patient data because of concerns about data security and confidentiality. To address this, various privacy-preserving methods, including synthetic data generation, have been proposed. Synthetic data replicate existing data as closely as possible, acting as a proxy for sensitive information. While patient data are often longitudinal, this aspect remains underrepresented in existing reviews of synthetic data generation in healthcare. This paper maps and describes methods for generating and evaluating synthetic longitudinal patient data in real-life settings through a systematic literature review, conducted following the PRISMA guidelines and incorporating data from five databases up to May 2024. Thirty-nine methods were identified, with four addressing all challenges of longitudinal data generation, though none included privacy-preserving mechanisms. Resemblance was evaluated in most studies, utility in the majority, and privacy in just over half. Only a small fraction of studies assessed all three aspects. Our findings highlight the need for further research in this area.