Stephanie L. Hyland

According to our database, Stephanie L. Hyland authored at least 29 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology.

Nur Yildirim, Hannah Richardson, Maria T. A. Wetscherek, Junaid Bajwa, Joseph Jacob, Mark A. Pinnock, Stephen Harris, Daniel Coelho de Castro, Shruthi Bannur, Stephanie L. Hyland, Pratik Ghosh, Mercy Ranjit, Kenza Bouzid, Anton Schwaighofer, Fernando Pérez-García, Harshita Sharma, Ozan Oktay, Matthew P. Lungren, Javier Alvarez-Valle, Aditya V. Nori, Anja Thieme

CoRR, 2024

RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision.

Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor, Kenza Bouzid, Valentina Salvatelli, Maximilian Ilse, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Matthew P. Lungren, Maria Wetscherek, Noel Codella, Stephanie L. Hyland, Javier Alvarez-Valle, Ozan Oktay

CoRR, 2024

2023

Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-technical Perspective: Socio-technical perspectives on hypotension prediction.

Pratik Ghosh, Karen L. Posner, Stephanie L. Hyland, Wil van Cleve, Melissa Bristow, Dustin R. Long, Konstantina Palla, Bala Nair, Christine Fong, Ronald Pauldine, Monica S. Vavilala, Kenton O'Hara

ACM Trans. Comput. Hum. Interact., October, 2023

MAIRA-1: A specialised large multimodal model for radiology report generation.

Stephanie L. Hyland, Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, Mercy Ranjit, Anton Schwaighofer, Fernando Pérez-García, Valentina Salvatelli, Shaury Srivastav, Anja Thieme, Noel Codella, Matthew P. Lungren, Maria Teodora Wetscherek, Ozan Oktay, Javier Alvarez-Valle

CoRR, 2023

Compositional Zero-Shot Domain Transfer with Text-to-Text Models.

Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya V. Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland

CoRR, 2023

Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.

Antonia Tsvetanova, Matthew Sperrin, David A. Jenkins, Niels Peek, Iain E. Buchan, Stephanie L. Hyland, Glen P. Martin

Proceedings of the MEDINFO 2023 - The Future Is Accessible, 2023

Exploring the Boundaries of GPT-4 in Radiology.

Qianchu Liu, Stephanie L. Hyland, Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, Maria Wetscherek, Robert Tinn, Harshita Sharma, Fernando Pérez-García, Anton Schwaighofer, Pranav Rajpurkar, Sameer Tajdin Khanna, Hoifung Poon, Naoto Usuyama, Anja Thieme, Aditya V. Nori, Matthew P. Lungren, Ozan Oktay, Javier Alvarez-Valle

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing.

Shruthi Bannur, Stephanie L. Hyland, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, Anton Schwaighofer, Maria Wetscherek, Matthew P. Lungren, Aditya V. Nori, Javier Alvarez-Valle, Ozan Oktay

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Looking for Out-of-Distribution Environments in Critical Care: A case study with the eICU Database.

Dimitris Spathis, Stephanie L. Hyland

CoRR, 2022

Predicting the impact of treatments over time with uncertainty aware neural differential equations.

Edward De Brouwer, Javier González Hernández, Stephanie L. Hyland

CoRR, 2022

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing.

Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie L. Hyland, Maria Wetscherek, Tristan Naumann, Aditya V. Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay

Proceedings of the Computer Vision - ECCV 2022, 2022

Predicting the impact of treatments over time with uncertainty aware neural differential equations.

Edward De Brouwer, Javier Gonzalez, Stephanie L. Hyland

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Early prediction of respiratory failure in the intensive care unit.

Matthias Hüser, Martin Faltys, Xinrui Lyu, Chris Barber, Stephanie L. Hyland, Tobias M. Merz, Gunnar Rätsch

CoRR, 2021

Inconsistencies in handling missing data across stages of prediction modelling: a review of methods used.

Antonia Tsvetanova, Matthew Sperrin, Niels Peek, Iain E. Buchan, Stephanie L. Hyland, Glen P. Martin

Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021

Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit.

Emma Rocheteau, Pietro Liò, Stephanie L. Hyland

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020

ML4H Abstract Track 2020.

Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Suproteem K. Sarkar, Subhrajit Roy, Stephanie L. Hyland

CoRR, 2020

Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks.

Emma Rocheteau, Pietro Liò, Stephanie L. Hyland

CoRR, 2020

Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All.

Suproteem K. Sarkar, Subhrajit Roy, Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Ioana Bica, Griffin Adams, Stephen Pfohl, Stephanie L. Hyland

Proceedings of the Machine Learning for Health Workshop, 2020

2019

On the Intrinsic Privacy of Stochastic Gradient Descent.

Stephanie L. Hyland, Shruti Tople

CoRR, 2019

Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies.

Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong-Van Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch

CoRR, 2019

Machine learning for early prediction of circulatory failure in the intensive care unit.

Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean A. Bodenham, Karsten M. Borgwardt, Gunnar Rätsch, Tobias M. Merz

CoRR, 2019

2018

Improving Clinical Predictions through Unsupervised Time Series Representation Learning.

Xinrui Lyu, Matthias Hüser, Stephanie L. Hyland, George Zerveas, Gunnar Rätsch

CoRR, 2018

Predicting circulatory system deterioration in intensive care unit patients.

Stephanie L. Hyland, Matthias Hüser, Xinrui Lyu, Martin Faltys, Tobias Merz, Gunnar Rätsch

Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), 2018

A Machine Learning-based Early Warning System for Circulatory System Deterioration in Intensive Care Unit Patients.

Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Cristóbal Esteban, Gunnar Rätsch, Tobias Merz

Proceedings of the AMIA 2018, 2018

2017

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs.

Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch

CoRR, 2017

Learning Unitary Operators with Help From u(n).

Stephanie L. Hyland, Gunnar Rätsch

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Knowledge Transfer with Medical Language Embeddings.

Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch

CoRR, 2016

Neural Document Embeddings for Intensive Care Patient Mortality Prediction.

Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland, Carsten Eickhoff

CoRR, 2016

A Generative Model of Words and Relationships from Multiple Sources.

Stephanie L. Hyland, Theofanis Karaletsos, Gunnar Rätsch

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016