Thoughts and adventures in data science and machine learning
© 2022. All rights reserved.
Thoughts and adventures in data science and machine learning
© 2022. All rights reserved.
Fleuren, L.M., Dam, T.A., Tonutti, M. et al. Predictors for extubation failure in COVID-19 patients using a machine learning approach. Critical Care 25, 448 (2021). https://doi.org/10.1186/s13054-021-03864-3
Thoral, P. J., Fornasa, M., de Bruin, D. P., Tonutti, M., et al. Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists. Critical care explorations, 3(9), e0529 (2021). https://doi.org/10.1097/CCE.0000000000000529
Fleuren, L.M.*, Tonutti, M.* et al. 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 Warehouse. ICMx 9, 32 (2021). https://doi.org/10.1186/s40635-021-00397-5 (*co-first author)
Fleuren, L.M., Dam, T.A., Tonutti, M. et al. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients. Crit Care 25, 304 (2021). https://doi.org/10.1186/s13054-021-03733-z
Fleuren, L.M., de Bruin, D.P., Tonutti, M. et al. Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse. Intensive Care Med (2021). https://doi.org/10.1007/s00134-021-06361-x
Ruhe D., Cinà G., Tonutti M., de Bruin D., Elbers P. (2019). Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications. AI For Social Good Workshop @ ICML 2019. [Paper] - [Poster] - [Code]
Tonutti M., Ruffaldi E., Cattaneo A., Avizzano CA (2019). Robust and subject-independent driving manoeuvre anticipation through Domain-Adversarial Recurrent Neural Networks. Robotics and Autonomous Systems, 115, 162-173. https://doi.org/10.1016/j.robot.2019.02.007. [Paper (PDF)] - [Code]
Tonutti M., Gras G., & Yang G. Z. (2017). A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery. Artificial Intelligence in Medicine, 80, 39-47 https://doi.org/10.1016/j.artmed.2017.07.004. [Paper (PDF)] - [Code]
Tonutti M., Elson D. S., Yang G. Z., Darzi A. W., & Sodergren M. H. (2016). The role of technology in minimally invasive surgery: state of the art, recent developments and future directions. Postgraduate medical journal, 93(1097), 159. http://dx.doi.org/10.1136/postgradmedj-2016-134311 [Paper (PDF)]
Sensible Local Interpretations via Class-Weight Uncertainty and Conditional Perturbation. [Paper] [Code]
Uncertainty estimation for classification and risk prediction in medical settings [Paper]