ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Time-series Analysis


  1. 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.
  2. J. Yoon, D. Jarrett, M. van der Schaar, "Time-series Generative Adversarial Networks," Neural Information Processing Systems (NeurIPS), 2019. [Link] [Supplementary Materials]
  3. A. Bellot, M. van der Schaar, "Conditional Independence Testing using Generative Adversarial Networks," Neural Information Processing Systems (NeurIPS), 2019. [Link] [Supplementary Materials]
  4. A. M. Alaa, M. van der Schaar, "Attentive State-Space Modeling of Disease Progression," Neural Information Processing Systems (NeurIPS), 2019. [Link]
  5. 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]
  6. J. Yoon, J. Jordon, M. van der Schaar, "ASAC: Active Sensing using Actor-Critic Models," Machine Learning for Healthcare Conference (MLHC), 2019. [Link]
  7. 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]
  8. 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]
  9. 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]
  10. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  11. 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
  12. 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]
  13. 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
  14. 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]
  15. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  16. K. Ahuja, W. R. Zame, M. van der Schaar, "DPSCREEN: Dynamic Personalized Screening," NIPS, 2017. [Link][Poster]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. A. M. Alaa and M. van der Schaar, "Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition ," NIPS, 2016. [Link] [Poster]
  25. 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]
  26. 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]
  27. 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]