# Synthetic Patient Generation: A Deep Learning Approach Using Variational Autoencoders

arXiv: https://arxiv.org/abs/1808.06444 · HTML: https://mlsanity.com/paper/1808.06444/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [Synthetic Data Generation for Augmenting Small Samples](https://mlsanity.com/paper/2501.18741/) (2025)
- [Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios](https://mlsanity.com/paper/2407.03080/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)

## Abstract (arXiv)

Artificial Intelligence in healthcare is a new and exciting frontier and the possibilities are endless. With deep learning approaches beating human performances in many areas, the logical next step is to attempt their application in the health space. For these and other Machine Learning approaches to produce good results and have their potential realized, the need for, and importance of, large amounts of accurate data is second to none. This is a challenge faced by many industries and more so in the healthcare space. We present an approach of using Variational Autoencoders (VAE's) as an approach to generating more data for training deeper networks, as well as uncovering underlying patterns in diagnoses and the patients suffering from them. By training a VAE, on available data, it was able to learn the latent distribution of the patient features given the diagnosis. It is then possible, after training, to sample from the learnt latent distribution to generate new accurate patient records given the patient diagnosis.