no code implementations • 23 Mar 2023 • Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
no code implementations • 26 May 2022 • Dimitris Spathis, Stephanie L. Hyland
Clinical machine learning models show a significant performance drop when tested in settings not seen during training.
no code implementations • 12 May 2021 • Matthias Hüser, Martin Faltys, Xinrui Lyu, Chris Barber, Stephanie L. Hyland, Tobias M. Merz, Gunnar Rätsch
The development of respiratory failure is common among patients in intensive care units (ICU).
no code implementations • 19 Nov 2020 • Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Suproteem K. Sarkar, Subhrajit Roy, Stephanie L. Hyland
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2020.
1 code implementation • 5 Dec 2019 • Stephanie L. Hyland, Shruti Tople
Introducing noise in the training of machine learning systems is a powerful way to protect individual privacy via differential privacy guarantees, but comes at a cost to utility.
no code implementations • 29 Apr 2019 • Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier, Kjong Lehmann, Andreas Wicki, Fernando Perez Cruz, Julia E. Vogt, Gunnar Rätsch
To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4, 007 cancer patients and their tumors.
no code implementations • 16 Apr 2019 • Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
no code implementations • 2 Dec 2018 • Xinrui Lyu, Matthias Hueser, Stephanie L. Hyland, George Zerveas, Gunnar Raetsch
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making.
6 code implementations • ICLR 2018 • Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa.
1 code implementation • 1 Dec 2016 • Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland, Carsten Eickhoff
We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes.
1 code implementation • 17 Jul 2016 • Stephanie L. Hyland, Gunnar Rätsch
A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem.
no code implementations • 10 Feb 2016 • Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
Identifying relationships between concepts is a key aspect of scientific knowledge synthesis.
no code implementations • 1 Oct 2015 • Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch
We propose a generative model which integrates evidence from diverse data sources, enabling the sharing of semantic information.