ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Treatment and trials

  1. 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]
  2. 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]
  3. O. Atan, W. R. Zame, M. van der Schaar, "Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation," 2018. [Link]
  4. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  5. A. M. Alaa, M. van der Schaar, "Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design," ICML, 2018. [Link]
  6. 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]
  7. E. Cenko, O. Manfrini, S Kedev, G Stankovic, Z Vasiljevic, M. van der Schaar, J. Yoon, M. Vavlukis, O. Kalpak, D. Milicic, A. Koller, L. Badimon, R. Bugiardini, "Sex difference in the impact of delay to reperfusion on coronary blood flow and outcomes in ST-segment elevation myocardial infarction," European Society of Cardiology, 2018. [Link]
  8. O. Atan, J. Jordon, M. van der Schaar, "Deep-Treat: Learning Optimal Personalized Treatments from Observational Data using Neural Networks," AAAI, 2018. [Link]
  9. R. Bugiardini, E. Cenko, J. Yoon, B. Ricci, D. Milicic, S. Kedev, Z. Vasiljevic, O. Manfrini, M. van der Schaar, L. Badimon, "Late PCI in STEMI: A Complex Interaction between Delay and Age," American College of the Cardiology (ACC) - 67th Annual Scientific Session & Expo - Orlando; Journal of the American College of Cardiology, 71 (11 Supplement) A44, Mar 2018. [Link]
  10. B. Ricci, M. van der Schaar, J. Yoon, E. Cenko, Z. Vasiljevic, M. Dorobantu, M. Zdravkovic, S. Kedev, O. Kalpak, D. Milicic, O. Manfrini, L. Badimon, R. Bugiardini, "Machine Learning Techniques for Risk Stratification of Non-ST-Elevation Acute Coronary Syndrome: The Role of Diabetes and Age," American Heart Association Scientific Session, 2017 - Circulation, 2017; 136:A15892. [Link]
  11. 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]
  12. M. K. Ross, J. Yoon, K. Moon, M. van der Schaar, "A Personalized Approach to Asthma Control Over Time: Discovering Phenotypes Using Machine Learning," American Thoracic Society (ATS) International Conference, 2017. [Link]
  13. A. M. Alaa, M. van der Schaar, "Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes," NIPS, 2017. [Link] [Supplementary Materials]
  14. J. Yoon, C. Davtyan, M. van der Schaar, "Discovery and Clinical Decision Support for Personalized Healthcare," IEEE J. Biomedical and Health Informatics, 2016. [Link]