| The role of technology in minimally invasive surgery: state of the art, recent developments and future directions M Tonutti, DS Elson, GZ Yang, AW Darzi, MH Sodergren Postgraduate medical journal 93 (1097), 159-167, 2017 | 136 | 2017 |
| A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery M Tonutti, G Gras, GZ Yang Artificial intelligence in medicine 80, 39-47, 2017 | 102 | 2017 |
| Robust and subject-independent driving manoeuvre anticipation through Domain-Adversarial Recurrent Neural Networks M Tonutti, E Ruffaldi, A Cattaneo, CA Avizzano Robotics and Autonomous Systems 115, 162-173, 2019 | 43 | 2019 |
| The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients LM Fleuren, TA Dam, M Tonutti, DP de Bruin, RCA Lalisang, D Gommers, ... Critical Care 25, 1-12, 2021 | 42 | 2021 |
| Explainable machine learning on AmsterdamUMCdb for ICU discharge decision support: uniting intensivists and data scientists PJ Thoral, M Fornasa, DP de Bruin, M Tonutti, H Hovenkamp, ... Critical care explorations 3 (9), e0529, 2021 | 40 | 2021 |
| Predictors for extubation failure in COVID-19 patients using a machine learning approach LM Fleuren, TA Dam, M Tonutti, DP de Bruin, RCA Lalisang, D Gommers, ... Critical Care 25, 1-10, 2021 | 34 | 2021 |
| Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse LM Fleuren, DP de Bruin, M Tonutti, RCA Lalisang, PWG Elbers Intensive care medicine 47 (4), 478-481, 2021 | 22* | 2021 |
| Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications D Ruhe, G Cinà, M Tonutti, D de Bruin, P Elbers AI for Social Good Workshop @ ICML 2019, 2019 | 18 | 2019 |
| Uncertainty estimation for classification and risk prediction in medical settings L Meijerink, G Ciná, M Tonutti arXiv preprint arXiv:2004.05824, 2020 | 14* | 2020 |
| Some patients are more equal than others: variation in ventilator settings for coronavirus disease 2019 acute respiratory distress syndrome TA Dam, HJ de Grooth, T Klausch, LM Fleuren, DP de Bruin, R Entjes, ... Critical care explorations 3 (10), e0555, 2021 | 11 | 2021 |
| Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data … LM Fleuren, M Tonutti, DP de Bruin, RCA Lalisang, TA Dam, D Gommers, ... Intensive care medicine experimental 9 (1), 1-15, 2021 | 7 | 2021 |
| Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: a retrospective cohort study in the Netherlands comparing the value of … I Vagliano, MC Schut, A Abu-Hanna, DA Dongelmans, DW De Lange, ... International Journal of Medical Informatics 167, 104863, 2022 | 5 | 2022 |
| Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study JM Smit, JH Krijthe, H Endeman, AN Tintu, YB de Rijke, D Gommers, ... Intelligence-based medicine 6, 100071, 2022 | 5 | 2022 |
| A pragmatic approach to estimating average treatment effects from EHR data: The effect of prone positioning on mechanically ventilated COVID-19 patients A Izdebski, PJ Thoral, RCA Lalisang, DM McHugh, D Gommers, ... arXiv preprint arXiv:2109.06707, 2021 | 2 | 2021 |
| The Companion Crutch: Design and Development of a Wireless Instrumented Crutch M Tonutti Imperial College London, 2015 | 2 | 2015 |
| A data-driven method for real-time modelling of brain tumour deformation M Tonutti Imperial College London, 2016 | 1 | 2016 |
Paul WG ElbersIntensivist, Amsterdam UMCPreverjeni e-poštni naslov na amsterdamumc.nl
