Publications from Laboratory of AI for Digital Health | Engineering Science Department | University of Oxford
Publications
Latest Publications
- Zhu, Mingcheng, et al. "From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction"Forty-third International Conference on Machine Learning. 2026. paper, code
- Li, Chenqi, et al. "BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals." The Fourteenth International Conference on Learning Representations. 2026. paper, code
- Chen, Tianyi, et al. "Cross-representation benchmarking in time-series electronic health records for clinical outcome prediction." ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2026. paper, code
- Gögl, Moritz, et al. "DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments." The Thirty-ninth Annual Conference on Neural Information Processing Systems. 2025. paper, code
- 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
- Yangyang, Xu, et al. "Cross-Subject Mind Decoding from Inaccurate Representations." Proceedings of the ieee/cvf international conference on computer vision. 2025. paper
- Liu, Yu, et al. "SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis." Proceedings of the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2025. paper, code
- 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
- Luo, Zhiyao, et al. "Position: reinforcement learning in dynamic treatment regimes needs critical reexamination." Proceedings of the 41st International Conference on Machine Learning. 2024. paper, code
Mesinovic, Munib, Max Buhlan, and Tingting Zhu. "Causal graph neural networks for healthcare." Nature Biomedical Engineering (2026): 1-21. paper
Mesinovic, Munib, et al. "DynaGraph: interpretable dynamic graph learning for temporal electronic health records." npj Digital Medicine (2026). paper
Zhu, Mingcheng, et al. "Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction." npj Digital Medicine (2026). paper, code
Mesinovic, Munib, et al. "Foundation models enable wearable signal screening for cardiovascular disease among people living with HIV." Communications Medicine (2026). paper
Mesinovic, Munib, Peter Watkinson, and Tingting Zhu. "Explainability in the age of large language models for healthcare." Communications Engineering 4.1 (2025): 128. paper
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
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
Yuan, Kevin, et al. "Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections." Journal of Infection 90.2 (2025): 106388. paper
Zhang, Shuo, et al. "Student loss: Towards the probability assumption in inaccurate supervision." IEEE Transactions on Pattern Analysis and Machine Intelligence 46.6 (2024): 4460-4475. paper, code
Mesinovic, Munib, Peter Watkinson, and Tingting Zhu. "DySurv: dynamic deep learning model for survival analysis with conditional variational inference." Journal of the American Medical Informatics Association (2024): ocae271. 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
Li, Chenqi, Timothy Denison, and Tingting Zhu. "A Survey of Few-Shot Learning for Biomedical Time Series." IEEE Reviews in Biomedical Engineering (2024). paper
- Luo, Zhiyao, and Tingting Zhu. "Are Large Language Models Dynamic Treatment Planners? An In Silico Study from a Prior Knowledge Injection Angle." arXiv preprint arXiv:2508.04755 (2025). paper
- Liu, Yu, et al. "Identifying profiles, trajectories, burden, social and biological factors in 3.3 million individuals with multimorbidity in England." medRxiv (2025): 2025-05. paper
- Mesinovic, Munib, et al. "DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation." arXiv preprint arXiv:2503.22257 (2025). paper
- Ghosheh, Ghadeer O., Jin Li, and Tingting Zhu. "Understanding Missingness in Time-series Electronic Health Records for Individualized Representation." arXiv preprint arXiv:2402.15730 (2024). paper
- Luo, Zhiyao, et al. "DTR-bench: an in silico environment and benchmark platform for reinforcement learning based dynamic treatment regime." arXiv preprint arXiv:2405.18610 (2024). paper
- Ghosheh, Ghadeer O., Jin Li, and Tingting Zhu. "Understanding Missingness in Time-series Electronic Health Records for Individualised Representation." arXiv preprint arXiv:2402.15730 (2024). 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
All Publications
A Review of Deep Learning for Individualized Treatment Effect Estimation in Healthcare Time Series
Cai Z, Liu Y & Zhu T (2026)
BibTeX
@misc{areviewofdeeple-2026/4,
title={A Review of Deep Learning for Individualized Treatment Effect Estimation in Healthcare Time Series},
author={Cai Z, Liu Y & Zhu T},
year = "2026"
}
Interpretable multimodal machine learning model for predicting health risks of patients with heart failure
Chae R, Zhou J, Chou OHI, Yang B, Pu H et al. (2026), Methods
BibTeX
@article{interpretablemu-2026/2,
title={Interpretable multimodal machine learning model for predicting health risks of patients with heart failure},
author={Chae R, Zhou J, Chou OHI, Yang B, Pu H et al.},
journal={Methods},
publisher={Elsevier},
year = "2026"
}
Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction
Zhu M, Liu Y, Luo Z & Zhu T (2026), npj Digital Medicine, 9(1)
BibTeX
@article{bridgingdatagap-2026/1,
title={Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction},
author={Zhu M, Liu Y, Luo Z & Zhu T},
journal={npj Digital Medicine},
volume={9},
number={7},
publisher={Nature Research},
year = "2026"
}
Foundation models enable wearable signal screening for cardiovascular disease among people living with HIV.
Mesinovic M, Bich HH, Trieu LV, Quoc VN, Thanh NN et al. (2026), Communications medicine
BibTeX
@article{foundationmodel-2026/1,
title={Foundation models enable wearable signal screening for cardiovascular disease among people living with HIV.},
author={Mesinovic M, Bich HH, Trieu LV, Quoc VN, Thanh NN et al.},
journal={Communications medicine},
year = "2026"
}
Causal Graph Neural Networks for Healthcare
Mesinovic M, Buhlan M & Zhu T (2026)
BibTeX
@misc{causalgraphneur-2026/1,
title={Causal Graph Neural Networks for Healthcare},
author={Mesinovic M, Buhlan M & Zhu T},
year = "2026"
}