---
title: "Vironix Health Accepted to ML4H 2025 & NeurIPS TS4H for Privacy-Preserving Generative Health Data Modeling — Vironix"
description: "🚀 Major milestone for Vironix! We’re thrilled to share that our research on privacy-preserving generative modeling for chronic disease data has been…"
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canonical: "https://www.vironix.ai/news/vironix-health-accepted-to-ml4h-2025-neurips-ts4h-for-privacy-preserving-generative-health-data-modeling"
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Nov 18, 2025 · [Vironix News](https://www.vironix.ai/category/news.md) · 2 min read

# Vironix Health Accepted to ML4H 2025 & NeurIPS TS4H for Privacy-Preserving Generative Health Data Modeling

🚀 Major milestone for Vironix! We’re thrilled to share that our research on privacy-preserving generative modeling for chronic disease data has been…

![Vironix Health Accepted to ML4H 2025 & NeurIPS TS4H for Privacy-Preserving Generative Health Data Modeling](https://www.vironix.ai/_astro/Screenshot%202025-11-14%20at%2010.22.31%E2%80%AFAM.BRjOZKzI.png)

🚀 Major milestone for Vironix!

We’re thrilled to share that our research on privacy-preserving generative modeling for chronic disease data has been officially accepted to ML4H 2025 and the [NeurIPS](https://www.linkedin.com/company/nipsconference/) 2025 Time Series for Health (TS4H) Workshop — two of the most respected venues in machine learning and healthcare innovation.

Our paper, “Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease,” introduces a new synthetic data model that helps researchers and clinicians study chronic diseases without exposing any real patient information.

Using our differentially private model, DP-TimeGAN, we’re able to generate highly realistic, longitudinal synthetic health records that maintain the critical patterns clinicians rely on — while mathematically guaranteeing privacy. In validation studies, clinical reviewers could not distinguish synthetic data from real-world records.

👉 Why this matters: Today, less than 5% of healthcare data is ever used for research because privacy constraints make sharing and collaboration incredibly difficult.

Synthetic data like this could unlock the other 95% safely, enabling better tools, smarter triage, more accurate prediction models, and ultimately: more proactive care for patients living with chronic conditions like heart failure, hypertension, COPD, diabetes, Crohn’s disease, kidney disease, and more.

This work represents a major step toward building clinical AI systems that are private by design and ready for real-world deployment and impact.

We’re proud of our team, including our contributors at the [University of Oxford](https://www.linkedin.com/company/oxforduni/), for this accomplishment and excited to continue pushing the boundaries of what’s possible in responsible healthcare AI.

Read the full press release here: [https://lnkd.in/g8b\_sDRt](https://lnkd.in/g8b_sDRt)

Sources: [https://lnkd.in/gWqEgAaM](https://lnkd.in/gWqEgAaM)

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