Stephanie L Hyland
Stephanie L Hyland
Microsoft Research Cambridge
Verified email at - Homepage
Cited by
Cited by
Real-valued (medical) time series generation with recurrent conditional gans
C Esteban, SL Hyland, G Rätsch
arXiv preprint arXiv:1706.02633, 2017
Early prediction of circulatory failure in the intensive care unit using machine learning
SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ...
Nature medicine 26 (3), 364-373, 2020
Identification of active transcriptional regulatory elements from GRO-seq data
CG Danko, SL Hyland, LJ Core, AL Martins, CT Waters, HW Lee, ...
Nature methods 12 (5), 433-438, 2015
A global metagenomic map of urban microbiomes and antimicrobial resistance
D Danko, D Bezdan, EE Afshin, S Ahsanuddin, C Bhattacharya, DJ Butler, ...
Cell 184 (13), 3376-3393. e17, 2021
Making the most of text semantics to improve biomedical vision–language processing
B Boecking, N Usuyama, S Bannur, DC Castro, A Schwaighofer, S Hyland, ...
European conference on computer vision, 1-21, 2022
Neural document embeddings for intensive care patient mortality prediction
P Grnarova, F Schmidt, SL Hyland, C Eickhoff
arXiv preprint arXiv:1612.00467, 2016
Improving clinical predictions through unsupervised time series representation learning
X Lyu, M Hueser, SL Hyland, G Zerveas, G Raetsch
arXiv preprint arXiv:1812.00490, 2018
Learning Unitary Operators with Help From u (n)
SL Hyland, G Rätsch
AAAI 2017, 2016
Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit
E Rocheteau, P Liò, S Hyland
Proceedings of the conference on health, inference, and learning, 58-68, 2021
Learning to exploit temporal structure for biomedical vision-language processing
S Bannur, S Hyland, Q Liu, F Perez-Garcia, M Ilse, DC Castro, B Boecking, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Leveraging electronic health records for data science: common pitfalls and how to avoid them
CM Sauer, LC Chen, SL Hyland, A Girbes, P Elbers, LA Celi
The Lancet Digital Health 4 (12), e893-e898, 2022
HiRID, a high time-resolution ICU dataset (version 1.1. 1)
M Faltys, M Zimmermann, X Lyu, M Hüser, S Hyland, G Rätsch, T Merz
Physio. Net 10, 2021
Missing data was handled inconsistently in UK prediction models: a review of method used
A Tsvetanova, M Sperrin, N Peek, I Buchan, S Hyland, GP Martin
Journal of Clinical Epidemiology 140, 149-158, 2021
Predicting the impact of treatments over time with uncertainty aware neural differential equations.
E De Brouwer, J Gonzalez, S Hyland
International Conference on Artificial Intelligence and Statistics, 4705-4722, 2022
Machine learning for health (ML4H) 2020: Advancing healthcare for all
SK Sarkar, S Roy, E Alsentzer, MBA McDermott, F Falck, I Bica, G Adams, ...
Machine Learning for Health, 1-11, 2020
Machine learning for early prediction of circulatory failure in the intensive care unit
SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ...
arXiv preprint arXiv:1904.07990, 2019
Intraoperative prediction of postanaesthesia care unit hypotension
K Palla, SL Hyland, K Posner, P Ghosh, B Nair, M Bristow, Y Paleva, ...
British Journal of Anaesthesia 128 (4), 623-635, 2022
A generative model of words and relationships from multiple sources
SL Hyland, T Karaletsos, G Rätsch
Association for the Advancement of Artificial Intelligence, 2016
On the intrinsic privacy of stochastic gradient descent
SL Hyland, S Tople
Preprint at https://arxiv. org/pdf/1912.02919. pdf, 2019
Exploring the Boundaries of GPT-4 in Radiology
Q Liu, S Hyland, S Bannur, K Bouzid, DC Castro, MT Wetscherek, R Tinn, ...
arXiv preprint arXiv:2310.14573, 2023
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