5 citations · 5 across the 2 of their papers we have counts for
8 papers
Hide-and-Seek Privacy Challenge
James Jordon, Daniel Jarrett, Jinsung Yoon +7
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identifi…
Adaptive Prediction Timing for Electronic Health Records
Jacob Deasy, Ari Ercole, Pietro Liò
In realistic scenarios, multivariate timeseries evolve over case-by-case time-scales. This is particularly clear in medicine, where the rate of clinical events varies by ward, pati…
Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning
Jacob Deasy, Ari Ercole, Pietro Liò
Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerti…
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
Jacob Deasy, Pietro Liò, Ari Ercole
We present a machine learning pipeline and model that uses the entire uncurated EHR for prediction of in-hospital mortality at arbitrary time intervals, using all available chart,…
Characterising complex healthcare systems using network science: The small world of emergency surgery
Katharina Kohler, Ari Ercole
Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Nevertheless, metrics of performance have to da…
DeepClean -- self-supervised artefact rejection for intensive care waveform data using deep generative learning
Tom Edinburgh, Peter Smielewski, Marek Czosnyka +2
Waveform physiological data is important in the treatment of critically ill patients in the intensive care unit. Such recordings are susceptible to artefacts, which must be removed…