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 abstracts


  1. B. A. Hemyari , J. Yoon , W. Zame , M. van der Schaar, M. Cadeiras, "Artificial Intelligence Improve Personalized Risk Prediction in High Risk Heart Failure Patients Post Cardiac Transplant," Circulation, 2018. [Link]
  2. A. M. Alaa , T. Bolton , E. D. Angelantonio , J. H. Rudd , M. van der Schaar, "Cardiovascular Disease Risk Prediction Using Machine Learning: A Prospective Cohort Study of 423,604 Participants," Circulation, 2018. [Link]
  3. C. Lee, M. van der Schaar, A. Floto, T. Daniels, "A Deep Learning Approach for Dynamic Survival Analysis with Competing Risk in CF," North American Cystic Fibrosis Conference, 2018. [Poster]
  4. B. Lim, T. Daniels, A. Floto, M. van der Schaar, "Forecasting Clinical Trajectories in Cystic Fibrosis using Deep Learning," North American Cystic Fibrosis Conference, 2018. [Poster]
  5. A. Alaa, M. van der Schaar, T. Daniels, A. Floto, "Machine Learning-Based Predictions of Prognosis in Cystic Fibrosis," North American Cystic Fibrosis Conference, 2018. [Poster]
  6. W. R. Zame, J. Yoon, F. Asselbergs, M. van der Schaar, "Interpretable Machine Learning Identifies Risk Predictors in Patients with Heart Failure," American Heart Association (AHA) Scientific Sessions, Circulation 2018. [Link1] [Link2]
  7. A. M. Alaa, T. Bolton, E. D. Angelantonio, J. H. F. Rudd, M. van der Schaar, "Cardiovascular Disease Risk Prediction Using Machine Learning: A Prospective Cohort Study of 423,604 Participants," American Heart Association (AHA) Scientific Sessions, 2018. [Link]
  8. A. M. Alaa, F. J. Gilbert, Y. Huang, M. van der Schaar, "Machine Learning for Identifying the Value of Digital Breast Tomosynthesis using Data from a Multicentre Retrospective Study," Radiological Society of North America (RSNA), 2018. [Link] [Presentation]
  9. Q. Feng, J. Yoon, M. van der Schaar, "ACW-RNN: Adaptive Clockwork Recurrent Neural Networks for Early Warning Systems in Hospitals," AI Med Europe Abstract Competition, 2018. [Link]
  10. A. Bellot, M. van der Schaar, "Boosting Competing Risks," AI Med Europe Abstract Competition, 2018. [Link]
  11. 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]
  12. 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]
  13. A. Banerjee, J. Yoon, W. R. Zame, M. Cadeiras, A. M. Alaa, M. van der Schaar, "Personalized Risk Prediction using Predictive Pursuit Machine Learning: A Pilot Study in Cardiac Transplantation," European Society of Cardiology Congress, 2017.- Selected as Best Posters in Advanced Heart Failure.
  14. J. Yoon, W. R. Zame, A. Banerjee, M. Cadeiras, A. M. Alaa, M. van der Schaar, "Personalized Risk Prediction using Predictive Pursuit Machine Learning: A Pilot Study in Cardiac Transplantation," Evidence Live Conference, 2017. [Link]
  15. 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]
  16. 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]