ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar
- All publications
- Recent NIPS, ICML, ICLR, AAAI, AISTATS conferences
- Recent Machine Learning for Healthcare (MLHC) conference
- Clinical publications
- Clinical abstracts
- Communications and Networks publications

    Clinical publications


  1. Edina Cenko, Mihaela van der Schaar, Jinsung Yoon, Olivia Manfrini, Zorana Vasiljevic, Marija Vavlukis, Sasko Kedev, Davor Milicic, Lina Badimon, Raffaele Bugiardini, " "De novo" heart failure: a mechanism underscoring sex differences in outcomes after ST-segment elevation myocardial infarction," Journal of the American College of Cardiology, 2019. [Link]
  2. 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]
  3. A. M. Alaa, T. Bolton, E. Di Angelantonio, J. H. F. Rudd, M. van der Schaar, "Cardiovascular Disease Risk Prediction using Automated Machine Learning: A Prospective Study of 423,604 UK Biobank Participants," PloS One, [Link]
  4. E. Cenko, M. van der Schaar, J. Yoon, S. Kedev, M. Valvukis, Z. Vasiljevic, M. Asanin, D. Milicic, O. Manfrini, L. Badimon, R. Bugiardini, "Sex-Specific Treatment Effects After Primary Percutaneous Intervention: A Study on Coronary Blood Flow and Delay to Hospital Presentation," Journal of the American Heart Association (JAHA), 2019. [Link]
  5. C. Lee, W. R. Zame, A. M. Alaa, M. van der Schaar, "Temporal Quilting for Survival Analysis," International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [Link] [Supplementary Materials]
  6. A. Bellot, M. van der Schaar, "Boosting Survival Predictions with Auxiliary Data from Heterogeneous Domains," International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [Link]
  7. O. Atan, W. R. Zame, M. van der Schaar, "Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials," International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [Link]
  8. J. Jordon, J. Yoon, M. van der Schaar, "KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks," International Conference on Learning Representations (ICLR), 2019. [Link] - Selected as oral presentation
  9. J. Yoon, J. Jordon, M. van der Schaar, "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019. [Link]
  10. J. Yoon, J. Jordon, M. van der Schaar, "PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees," International Conference on Learning Representations (ICLR), 2019. [Link]
  11. 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]
  12. C. Rietschel, J. Yoon, and M. van der Schaar, "Feature Selection for Survival Analysis with Competing Risks using Deep Learning," NIPS Machine Learning for Health Workshop 2018. [Link]
  13. C. Lee, N. Mastronarde, and M. van der Schaar, "Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  14. O. Lahav, N. Mastronarde, and M. van der Schaar, "What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  15. T. Kyono, F. J. Gilbert, and M. van der Schaar, "MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis," 2018. [Link]
  16. O. Atan, W. R. Zame, M. van der Schaar, "Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features," Machine Learning, 2018. [Link]
  17. 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]
  18. A. Bellot, M. van der Schaar, "Multitask Boosting for Survival Analysis with Competing Risks," NIPS, 2018.
  19. [Link]
  20. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  21. 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
  22. A. M. Alaa, M. van der Schaar, "Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning," Scientific Reports, 2018. [Link]
  23. 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]
  24. A. Nemchenko, T. Kyono, M. van der Schaar, "Siamese Survival Analysis with Competing Risks," International Conference on Artificial Neural Networks (ICANN), 2018. [Link]
  25. A. Bellot, M. van der Schaar, "Boosted Trees for Risk Prognosis," Machine Learning for Healthcare Conference (MLHC), 2018. [Link]
  26. 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
  27. J. Jordon, J. Yoon, M. van der Schaar, "Measuring the quality of Synthetic data for use in competitions," 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018. [Link]
  28. 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]
  29. O. Atan, W. R. Zame, M. van der Schaar, "Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks," ICML 2018 Causal Machine Learning Workshop, 2018. [Link]
  30. A. M. Alaa, M. van der Schaar, "Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms," IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2018. [Link]
  31. J. Yoon, J. Jordon, M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," ICML, 2018. [Link] [Appendix]
  32. J. Yoon, J. Jordon, M. van der Schaar, "RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks," ICML, 2018. [Link] [Appendix]
  33. A. M. Alaa, M. van der Schaar, "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning," ICML, 2018. [Link] [Webpage]
  34. A. M. Alaa, M. van der Schaar, "Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design," ICML, 2018. [Link]
  35. A. Bellot, M. van der Schaar, "A Hierarchical Bayesian Model for Personalized Survival Predictions," IEEE J. Biomedical and Health Informatics, 2018. [Link] [Supplementary Materials]
  36. A. M. Alaa, D. J. Llewellyn, C Routledge, M. van der Schaar, "Mnemosyne: A Decision Support System for Early Detection of Dementia," Submitted, 2018. [Link]
  37. E. Cenko, J. Yoon, S. Kedev, G. Stankovic, Z. Vasiljevic, G. Krljanac, O. Kalpak, B. Ricci, D. Milicic, O. Manfrini, M. van der Schaar, L. Badimon, R. Bugiardini, "Sex Differences in Outcomes After STEMI: Effect Modification by Treatment Strategy and Age," JAMA Internal Medicine, 2018. [Link]
  38. J. Yoon, W. R. Zame, A. Banerjee, M. Cadeiras, A. Alaa, M. van der Schaar, "Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation," PloS One, 2018. [Link] [Calculator Link]
  39. J. Yoon, W. R. Zame, M. van der Schaar, "ToPs: Ensemble Learning with Trees of Predictors," IEEE Transactions on Signal Processing (TSP), 2018. [Link]
  40. J. Yoon, J. Jordon, M. van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets," ICLR, 2018. [Link]
  41. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  42. A. Bellot, M. van der Schaar, "Tree-based Bayesian Mixture Model for Competing Risks," AISTATS, 2018. [Link]
  43. J. Yoon, W. R. Zame, M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks," 2017. [Link]
  44. C. Lee, W. R. Zame, J. Yoon, M. van der Schaar, "DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks," AAAI, 2018. [Link] [Supplementary Materials]
  45. O. Atan, J. Jordon, M. van der Schaar, "Deep-Treat: Learning Optimal Personalized Treatments from Observational Data using Neural Networks," AAAI, 2018. [Link]
  46. M. K. Ross, J. Yoon, M. van der Schaar, "Discovering Pediatric Asthma Phenotypes Based on Response to Controller Medication Using Machine Learning," Annals of the American Thoracic Society, 2017. [Link]
  47. A. M. Alaa, M. van der Schaar, "Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks," NIPS, 2017. [Link] - Selected as a spotlight paper
  48. A. M. Alaa, M. van der Schaar, "Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes," NIPS, 2017. [Link] [Supplementary Materials]
  49. K. Ahuja, W. R. Zame, M. van der Schaar, "DPSCREEN: Dynamic Personalized Screening," NIPS, 2017. [Link][Poster]
  50. O. Atan, W. R. Zame, Q. Feng, M. van der Schaar, "Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features," Submitted, 2017. [Link]
  51. 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]
  52. A. M. Alaa, M. Weisz, M. van der Schaar, "Deep Counterfactual Networks with Propensity-Dropout," ICML 2017 - Workshop on Principled Approaches to Deep Learning., 2017. [Link]
  53. 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]
  54. 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]
  55. 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]
  56. 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]
  57. 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]
  58. J. Yoon, A. M. Alaa, M. Cadeiras, M. van der Schaar, "Personalized Donor-Recipient Matching for Organ Transplantation," AAAI, 2017. [Link] [Poster]
  59. 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]
  60. C. Tekin, J. Yoon, and M. van der Schaar, "Adaptive Ensemble Learning with Confidence Bounds," IEEE Trans. Signal Process., 2016. [Link]
  61. A. M. Alaa and M. van der Schaar, "Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition ," NIPS, 2016. [Link] [Poster]
  62. 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]
  63. A. Alaa, K. H. Moon, W. Hsu and M. van der Schaar, "ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening," IEEE Transactions on Multimedia - Special Issue on Multimedia-based Healthcare, 2016. [Link]
  64. 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]
  65. 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]
  66. E. Soltanmohammadi, M. Naraghi-Pour, and M. van der Schaar, " Context-based Unsupervised Ensemble Learning and Feature Ranking," Machine Learning, pp. 1-27, June 2016. [Link]
  67. C. Tekin, J. Yoon, M. van der Schaar, "Adaptive ensemble learning with confidence bounds for personalized diagnosis," AAAI Workshop on Expanding the Boundaries of Health Informatics using AI (HIAI'16):Making Proactive, Personalized, and Participatory Medicine A Reality, 2016. [Link]
  68. J. Yoon, C. Davtyan, M. van der Schaar, "Discovery and Clinical Decision Support for Personalized Healthcare," IEEE J. Biomedical and Health Informatics, 2016. [Link]
  69. L. Song, W. Hsu, J. Xu and M. van der Schaar, "Using contextual learning to improve diagnostic accuracy: application in breast cancer screening," IEEE J. Biomedical and Health Informatics, 2015. [Link]
  70. E. Soltanmohammadi, M. Naraghi-Pour, M. van der Schaar, "Context-based Unsupervised Data Fusion for Decision Making," ICML, 2015. [Link]
  71. O. Atan, C. Tekin, J. Xu and M. van der Schaar, "Discovering Action-Dependent Relevance: Learning from Logged Data," Submitted, 2015. [Link]
  72. C. Tekin, O. Atan and M. van der Schaar, "Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis," IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 2, pp. 220 - 234, 2015. [Link]
  73. J. Xu, D. Sow, D. Turaga and M. van der Schaar, "Online Transfer Learning for Differential Diagnosis Determination," AAAI Workshop on the World Wide Web and Public Health Intelligence, 2015. [Link]
  74. C. Tekin and M. van der Schaar, "Active Learning in Context-Driven Stream Mining with an Application to Image Mining," IEEE Trans. Image Process., vol. 24, no. 11, pp. 3666-3679, 2015. [Link]
  75. O. Atan and M. van der Schaar, "Discover Relevant Sources : A Multi-Armed Bandit Approach," Submitted, 2015. [Link]
  76. M. Wolf, M. van der Schaar, H. Kim and J. Xu, "Analysis and Decision-Making in Caring Environments for Adults with Special Needs Adults," IEEE Design & Test, Special Issue on Cyber-Physical systems for Medical Applications, vol. 32, no. 5, Oct. 2015. [Link]
  77. J. Xu, J. Y. Xu, L. Song, G. Pottie, and M. van der Schaar, "Personalized Active Learning for Activity Classification using Wireless Wearable Sensors," IEEE Journal on Selected Topics in Signal Processing, 2016. [Link]