1. Integrating machine learning predictions for perioperative risk management: Towards an empirical design of a flexible-standardized risk assessment tool
2. Darryl Abrams Roberto Lorusso Jean-Louis Vincent and Daniel Brodie. 2020. ECMO during the COVID-19 pandemic: when is it unjustified? 3 pages. Darryl Abrams Roberto Lorusso Jean-Louis Vincent and Daniel Brodie. 2020. ECMO during the COVID-19 pandemic: when is it unjustified? 3 pages.
3. Ahmed M Alaa , Michael Weisz , and Mihaela Van Der Schaar . 2017. Deep counterfactual networks with propensity-dropout. arXiv preprint arXiv:1706.05966 ( 2017 ). Ahmed M Alaa, Michael Weisz, and Mihaela Van Der Schaar. 2017. Deep counterfactual networks with propensity-dropout. arXiv preprint arXiv:1706.05966 (2017).
4. Pietro Bertini , Fabio Guarracino , Marco Falcone , Pasquale Nardelli , Giovanni Landoni , Matteo Nocci , and Gianluca Paternoster . 2021. ECMO in COVID-19 patients: a systematic review and meta-analysis. Journal of cardiothoracic and vascular anesthesia ( 2021 ). Pietro Bertini, Fabio Guarracino, Marco Falcone, Pasquale Nardelli, Giovanni Landoni, Matteo Nocci, and Gianluca Paternoster. 2021. ECMO in COVID-19 patients: a systematic review and meta-analysis. Journal of cardiothoracic and vascular anesthesia (2021).
5. Ricky TQ Chen , Xuechen Li , Roger Grosse , and David Duvenaud . 2018. Isolating sources of disentanglement in variational autoencoders. arXiv preprint arXiv:1802.04942 ( 2018 ). Ricky TQ Chen, Xuechen Li, Roger Grosse, and David Duvenaud. 2018. Isolating sources of disentanglement in variational autoencoders. arXiv preprint arXiv:1802.04942 (2018).