no code implementations • 29 Dec 2023 • Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi, Anne-Mieke Vandamme, Valeria Ghisetti, Anders Sönnerborg, Maurizio Zazzi, Fabrizio Silvestri, Laura Palagi
We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios.
no code implementations • 8 Nov 2023 • Giulia Di Teodoro, Martin Pirkl, Francesca Incardona, Ilaria Vicenti, Anders Sönnerborg, Rolf Kaiser, Laura Palagi, Maurizio Zazzi, Thomas Lengauer
Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis.
1 code implementation • 18 Aug 2022 • Nicholas I-Hsien Kuo, Federico Garcia, Anders Sönnerborg, Maurizio Zazzi, Michael Böhm, Rolf Kaiser, Mark Polizzotto, Louisa Jorm, Sebastiano Barbieri
Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain.
1 code implementation • 12 Mar 2022 • Nicholas I-Hsien Kuo, Mark N. Polizzotto, Simon Finfer, Federico Garcia, Anders Sönnerborg, Maurizio Zazzi, Michael Böhm, Louisa Jorm, Sebastiano Barbieri
This has hampered the development of reproducible and generalisable machine learning applications in health care.
BIG-bench Machine Learning Generative Adversarial Network +1
no code implementations • 13 Jan 2021 • Melanie F. Pradier, Javier Zazo, Sonali Parbhoo, Roy H. Perlis, Maurizio Zazzi, Finale Doshi-Velez
We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance.
no code implementations • 13 Aug 2019 • Mike Wu, Sonali Parbhoo, Michael Hughes, Ryan Kindle, Leo Celi, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
The lack of interpretability remains a barrier to the adoption of deep neural networks.
2 code implementations • 16 Nov 2017 • Mike Wu, Michael C. Hughes, Sonali Parbhoo, Maurizio Zazzi, Volker Roth, Finale Doshi-Velez
The lack of interpretability remains a key barrier to the adoption of deep models in many applications.