# Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy

arXiv: https://arxiv.org/abs/2604.07557 · HTML: https://mlsanity.com/paper/2604.07557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results](https://mlsanity.com/paper/2305.07685/) (2023)
- [Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy](https://mlsanity.com/paper/2603.06875/) (2026)
- [Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention](https://mlsanity.com/paper/2603.14717/) (2026)
- [Generative Adversarial Networks with Limited Data: A Survey and Benchmarking](https://mlsanity.com/paper/2504.05456/) (2025)
- [Conditioning Protein Generation via Hopfield Pattern Multiplicity](https://mlsanity.com/paper/2603.20115/) (2026)

## Abstract (arXiv)

Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slow and the data are too sparse to train reliable models. We present multiplicity-weighted Stochastic Attention (SA), a generative framework based on modern Hopfield networks. Stochastic attention stores real patient profiles as memory patterns in a continuous energy landscape. The resulting distribution is a finite mixture with one component centered on each stored profile. We generated the reported cohort with Langevin dynamics; direct sampling from the same distribution reproduced its fidelity and membership-inference results. Multiplicity weights amplify selected subgroups at inference time without retraining. We applied the method to longitudinal coagulation data from 23 patients, with 72 features measured before pregnancy and during the first and third trimesters. The cohort included three patients with polycystic ovary syndrome and five who developed preeclampsia. Across statistical, structural, and mechanistic tests, including a separately specified coagulation model that was blind to the generator but calibrated on the same cohort, the synthetic profiles closely matched the real profiles under the measures applied at this sample size. A model calibrated on synthetic profiles predicted held-out real-patient TGA measurements as well as one calibrated on real profiles. These results provide proof of concept for using SA in the tested modeling tasks, including mechanistic calibration and hypothesis generation, with very small longitudinal cohorts.