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Representation Learning for Medical Time-Series

Representation Learning for Medical Time-Series

We develop models that learn robust, informative representations of longitudinal health data. Using graph neural networks, transformers, few-shot, and multimodal learning, we capture temporal and relational patterns in patient records, wearable signals, and clinical notes.

Publications

  1. Cai, Zi, et al. "ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions." Proceedings of the 34th ACM International Conference on Information and Knowledge Management. 2025. paper, code
  2. 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
  3. Mingcheng, Zhu, 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
  4. Mingcheng, Zhu, et al. "MedTPE: Compressing Long EHR Sequence for LLM-based Clinical Prediction with Token-Pair Encoding." KDD 2025 MILETS Workshop, 2025. paper  

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