Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit E Rocheteau, P Liņ, S Hyland Proceedings of the conference on health, inference, and learning, 58-68, 2021 | 71 | 2021 |
Predicting patient outcomes with graph representation learning C Tong*, E Rocheteau*, P Veličković, N Lane, P Liņ International Workshop on Health Intelligence, 281-293, 2021 | 64* | 2021 |
Forecasting ultra-early intensive care strain from COVID-19 in England J Deasy, E Rocheteau, K Kohler, DJ Stubbs, P Barbiero, P Liņ, A Ercole medRxiv, 2020 | 30 | 2020 |
On the role of artificial intelligence in psychiatry E Rocheteau The British Journal of Psychiatry 222 (2), 54-57, 2023 | 15 | 2023 |
Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs E Rocheteau*, D Kim* Machine Learning for Mobile Health (ML4MH) Workshop at NeurIPS, 2020 | 6 | 2020 |
Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients EC Rocheteau, I Bica, P Lio, A Ercole NeurIPS 2022 Workshop on Learning from Time Series for Health, 2022 | 4 | 2022 |
Representation Learning for Patients in the Intensive Care Unit E Rocheteau | 1 | 2022 |
How to organise a summer research placement E Rocheteau BMJ: British Medical Journal 358, 2017 | 1 | 2017 |
Machine Learning for Prediction of Childhood Mental Health Problems in Social Care R Crowley, K Parkin, EC Rocheteau, E Massou, Y Friedmann, A John, ... medRxiv, 2024.05. 03.24306756, 2024 | | 2024 |
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021 F Falck, Y Zhou, E Rocheteau, L Shen, L Oala, G Abebe, S Roy, S Pfohl, ... arXiv preprint arXiv:2112.00179, 2021 | | 2021 |
ICUnity: A software tool to harmonise the MIMIC-III and AmsterdamUMCdb databases E Rocheteau, J Deasy, LF Roggeveen, A Ercole Machine Learning for Healthcare Conference; Clinical Abstracts, 2020 | | 2020 |
What will British healthcare look like in 20 years’ time? E Rocheteau cmj, 003, 2017 | | 2017 |
Uncertainty Estimation for Sequence-to-Sequence Regression on Sparse Time Series S Vavaroutas, T Dang, E Rocheteau, C Mascolo | | |