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 officially accepted to ML4H 2025 and the NeurIPS 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, 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

Sources: https://lnkd.in/gWqEgAaM