Representation Learning for Medical Time-Series
Representation Learning for Medical Time-Series
Generative AI is transforming personalised healthcare by generating missing or future medical data to enhance clinical decision-making and tailor treatment plans. By leveraging deep learning, it enables precision diagnostics, predictive modelling of patient outcomes, and the creation of synthetic datasets for research and development. This innovative approach supports more adaptive, efficient, and personalised healthcare.
Publications
- Li, Chenqi, et al. "AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Feature Space-Guided Inversion." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 13. 2025. paper, code
- Yuan, Kevin, et al. "Transformers and large language models are efficient feature extractors for electronic health record studies." Communications Medicine 5.1 (2025): 83. paper, code
- Zhu, Mingcheng, et al. "Bridging Data Gaps of Rare Conditions in ICU: A Multi-Disease Adaptation Approach for Clinical Prediction." arXiv preprint arXiv:2507.06432 (2025). paper, code