Stephanie Hyland, MASt. Cambridge University (Applied Mathematics and Theoretical Physics)

"Explore the world. Nearly everything is really interesting if you go into it deeply enough." - Richard Feynman


My research focuses on the application and development of machine learning in healthcare.

I am interested in time series models (such as recurrent neural networks and Gaussian processes) appropriate for modelling physiological signals, and phenotyping through representation learning. I am also interested in the use of reinforcement learning techniques in healthcare.

I studied theoretical physics in Trinity College Dublin for my undergraduate degree, where I focused on lattice field theory. In 2012 went to Cambridge University (St. John’s College) to do Part III of the mathematical tripos in applied mathematics and theoretical physics. I then moved to New York to join the Tri-Institutional Training Program in Computational Biology. I spent a year at Cornell University before joining the Rätsch lab at Memorial Sloan Kettering Cancer Center in New York City, and relocated to Switzerland in 2016 when the group moved to ETHZ.

Abstract The deterioration of organ function in ICU patients requires swift response to prevent further damage to vital systems. Focusing on the circulatory system, we build a model to predict if a patient’s state will deteriorate in the near future. We identify circulatory system dys- function using the combination of excess lactic acid in the blood and low mean arterial blood pressure or the presence of vasoactive drugs. Using an observational cohort of 45,000 patients from a Swiss ICU, we extract and process patient time series and identify periods of circulatory system dysfunction to develop an early warning system. We train a gra- dient boosting model to perform binary classification every five minutes on whether the patient will deteriorate during an increasingly large win- dow into the future, up to the duration of a shift (8 hours). The model achieves an AUROC between 0.952 and 0.919 across the prediction win- dows, and an AUPRC between 0.223 and 0.384 for events with positive prevalence between 0.014 and 0.042. We also show preliminary results from a recurrent neural network. These results show that contemporary machine learning approaches combined with careful preprocessing of raw data collected during routine care yield clinically useful predictions in near real time [Workshop Abstract]

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

Submitted Proceedings of the First Joint Workshop on AI in Health


Abstract Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their application to medical data. RGANs make use of recurrent neural networks in the generator and the discriminator. In the case of RCGANs, both of these RNNs are conditioned on auxiliary information. We demonstrate our models in a set of toy datasets, where we show visually and quantitatively (using sample likelihood and maximum mean discrepancy) that they can successfully generate realistic time-series. 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. We illustrate with these metrics that RCGANs can generate time-series data useful for supervised training, with only minor degradation in performance on real test data. This is demonstrated on digit classification from 'serialised' MNIST and by training an early warning system on a medical dataset of 17,000 patients from an intensive care unit. We further discuss and analyse the privacy concerns that may arise when using RCGANs to generate realistic synthetic medical time series data.

Authors Stephanie L Hyland, Cristobal Esteban, Gunnar Rätsch

Submitted arXiv


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

Submitted NIPS Workshop on Machine Learning for Healthcare


Authors Stephanie L Hyland, Theofanis Karaletsos, Gunnar Rätsch

Submitted NIPS Workshop on Machine Learning for Healthcare, 2015


Authors Charles G Danko, Stephanie L Hyland, Leighton J Core, Andre L Martins, Colin T Waters, Hyung Won Lee, Vivian G Cheung, W Lee Kraus, John T Lis, Adam Siepel

Submitted Nature Methods