Foundation and Generative Models for Digital Health Data
Foundation and Generative Models for Digital Health Data
We build scalable foundation models and generative models to simulate patient trajectories and create digital twins. We also develop methods for cross-modality learning and adaptation in large models. These models support forecasting, imputation, and personalised medicine across a range of digital health applications.
Publications
- Ghosheh, Ghadeer O., Moritz Gögl, and Tingting Zhu. "A perspective on individualized treatment effects estimation from time-series health data." Journal of the American Medical Informatics Association (2025): ocae323. paper
- Ghosheh, Ghadeer O., Jin Li, and Tingting Zhu. "A survey of generative adversarial networks for synthesizing structured electronic health records." ACM Computing Surveys 56.6 (2024): 1-34. paper
- Ghosheh, Ghadeer O., Jin Li, and Tingting Zhu. "IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records." arXiv preprint arXiv:2401.04402 (2024). paper