# Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

arXiv: https://arxiv.org/abs/2605.17758 · HTML: https://mlsanity.com/paper/2605.17758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical synthetic data and agents, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 18 May 2026.

## Placed in

- Synthetic data and self-improvement for LLMs / [Medical synthetic data and agents](https://mlsanity.com/synth/medical-synthetic-data-and-agents/)

## Builds on (most cited first, 4 of 4)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation](https://mlsanity.com/paper/2506.19082/) (2025)
- [FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples](https://mlsanity.com/paper/2508.11810/) (2025)

## Abstract (arXiv)

Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information. Generating and evaluating synthetic data across privacy, utility, and fairness dimensions is crucial for enabling high-quality data availability in downstream prediction tasks and clinical decision making. We present Memisis, a tool that orchestrates and evaluates synthetic data by leveraging existing synthesis libraries, large language models (LLMs), and state-of-the-art evaluation metrics. Our tool creates a unified workflow for data generation, validation, and evaluation. Users can control training size, training epochs, and the number of synthetic rows to sample. Beyond manual configuration, an interactive agent mode allows users to specify data generation goals in natural language, and the tool orchestrates the full pipeline by invoking existing synthesizers while performing the requisite evaluation. For the demo, we use an open-source schizophrenia dataset with protected attributes related to race and gender, evaluate six synthesizers spanning GANs, VAEs, diffusion models, and normalizing flows, and use a local LLM to orchestrate the workflow. The system affords users flexibility and control over the data generation and evaluation process.