ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Time-series Analysis


  1. Z. Qian, A. M. Alaa, A. Bellot, J. Rashbass, M. der Schaar, "Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes," International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [Link]
  2. Y. Zhang, D. Jarrett, M. van der Schaar, "Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning," International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. [Link]
  3. D. Jarrett, M. van der Schaar, "Target-Embedding Autoencoders for Supervised Representation Learning," International Conference on Learning Representations (ICLR), 2020. [Link] - Selected as oral presentation
  4. I. Bica, A. M. Alaa, J. Jordon, M. van der Schaar, "Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations," International Conference on Learning Representations (ICLR), 2020. [Link] - Selected as spotlight presentation
  5. I. Bica, A. M. Alaa, M. van der Schaar, "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders," NeurIPS Machine Learning for Health Workshop, 2019.
  6. J. Yoon, D. Jarrett, M. van der Schaar, "Time-series Generative Adversarial Networks," Neural Information Processing Systems (NeurIPS), 2019. [Link] [Supplementary Materials]
  7. A. Bellot, M. van der Schaar, "Conditional Independence Testing using Generative Adversarial Networks," Neural Information Processing Systems (NeurIPS), 2019. [Link] [Supplementary Materials]
  8. A. M. Alaa, M. van der Schaar, "Attentive State-Space Modeling of Disease Progression," Neural Information Processing Systems (NeurIPS), 2019. [Link]
  9. D. Jarrett, J. Yoon, M. van der Schaar, " Dynamic Prediction in Clinical Survival Analysis using Temporal Convolutional Networks," IEEE J. Biomedical and Health Informatics, 2019. [Link]
  10. J. Yoon, J. Jordon, M. van der Schaar, "ASAC: Active Sensing using Actor-Critic Models," Machine Learning for Healthcare Conference (MLHC), 2019. [Link]
  11. C. Lee, J. Yoon, M. van der Schaar, "Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks based on Longitudinal Data," IEEE Transactions on Biomedical Engineering (TBME), 2019. [Link]
  12. D. Jarrett, J. Yoon, and M. van der Schaar, "MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  13. J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks," IEEE Transactions on Biomedical Engineering, 2018. [Link]
  14. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  15. J. Pohle, R. King, M. van der Schaar, R. Langrock, "Coupled Markov-switching regression: inference and a case study using electronic health record data," International Workshop on Statistical Modeling (IWSM), 2018. [Link] - Best student paper award
  16. E. Giunchiglia, A. Nemchenko, M. van der Schaar, "RNN-SURV: a Deep Recurrent Model for Survival Analysis," International Conference on Artificial Neural Networks (ICANN), 2018. [Link]
  17. B. Lim, M. van der Schaar, "Disease-Atlas: Navigating Disease Trajectories using Deep Learning," Machine Learning for Healthcare Conference (MLHC), 2018. [Link] [Presentation] - Best Paper Award in IJCAI-BOOM Workshop
  18. B. Lim, M. van der Schaar, ";Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning," 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018. [Link]
  19. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  20. K. Ahuja, W. R. Zame, M. van der Schaar, "DPSCREEN: Dynamic Personalized Screening," NIPS, 2017. [Link][Poster]
  21. J. Yoon, M. van der Schaar, "E-RNN: Entangled Recurrent Neural Networks for Causal Prediction," ICML 2017 - Workshop on Principled Approaches to Deep Learning., 2017. [Link]
  22. J. Yoon, W. R. Zame, M. van der Schaar, "Multi-directional Recurrent Neural Networks: A Novel Method for Estimating Missing Data," ICML 2017 - Time Series Workshop., 2017. [Link]
  23. A. M. Alaa, J. Yoon, S. Hu, and M. van der Schaar, "Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model," Big Data in Medicine: Tools, Transformation and Translation, Cambridge, 2017. [Link]
  24. A. M. Alaa, S. Hu, and M. van der Schaar, "Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis," ICML, 2017. [Link]
  25. A. Alaa, J. Yoon, S. Hu and M. van der Schaar, "Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes," IEEE Transactions on Biomedical Engineering, 2017. [Link]
  26. A. M. Alaa, J. Yoon, S. Hu, M. van der Schaar, "A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data," NIPS - Workshop on Machine Learning for Health, 2016. [Link]
  27. A. Alaa and M. van der Schaar, "A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference," Journal of Machine Learning Research (JMLR), 2017. [Link]
  28. A. M. Alaa and M. van der Schaar, "Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition ," NIPS, 2016. [Link] [Poster]
  29. W. Hoiles and M. van der Schaar, "A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics ," NIPS, 2016. [Link] [Poster]
  30. A. M. Alaa, J. Yoon, S. Hu, M. van der Schaar, "Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts," ICML 2016 - Workshop on Computational Frameworks for Personalization., 2016. [Link]
  31. J. Yoon, A. M. Alaa, S. Hu, M. van der Schaar, "ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission," ICML 2016. [Link]