28 citations · 84 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
ICU Disparnumerophobia and Triskaidekaphobia: The 'Irrational Care Unit'?
Ari Ercole
Whilst evidence-based medicine is the cornerstone of modern practice, it is likely that clinicians are influenced by cultural biases. This work set out to look for evidence of numb…
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Hiske Overweg, Anna-Lena Popkes, Ari Ercole +4
Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent yea…