-
Causality indices for bivariate time series data: a comparative review of performance
Inferring nonlinear and asymmetric causal relationships between multivar...
04/01/2021 ∙ by Tom Edinburgh, et al. ∙
0
∙
share
read it
-
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenge...
07/23/2020 ∙ by James Jordon, et al. ∙
11
∙
share
read it
-
Adaptive Prediction Timing for Electronic Health Records
In realistic scenarios, multivariate timeseries evolve over case-by-case...
03/05/2020 ∙ by Jacob Deasy, et al. ∙
11
∙
share
read it
-
Impact of novel aggregation methods for flexible, time-sensitive EHR prediction without variable selection or cleaning
Dynamic assessment of patient status (e.g. by an automated, continuously...
09/17/2019 ∙ by Jacob Deasy, et al. ∙
0
∙
share
read it
-
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
We present a machine learning pipeline and model that uses the entire un...
09/13/2019 ∙ by Jacob Deasy, et al. ∙
0
∙
share
read it
-
DeepClean -- self-supervised artefact rejection for intensive care waveform data using generative deep learning
Waveform physiological data is important in the treatment of critically ...
08/08/2019 ∙ by Tom Edinburgh, et al. ∙
0
∙
share
read it
-
Characterising complex healthcare systems using network science: The small world of emergency surgery
Hospitals are complex systems and optimising their function is critical ...
08/05/2019 ∙ by Katharina Kohler, et al. ∙
0
∙
share
read it
-
ICU Disparnumerophobia and Triskaidekaphobia: The 'Irrational Care Unit'?
Whilst evidence-based medicine is the cornerstone of modern practice, it...
07/01/2019 ∙ by Ari Ercole, et al. ∙
0
∙
share
read it
-
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
05/07/2019 ∙ by Anna-Lena Popkes, et al. ∙
12
∙
share
read it
