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Causality indices for bivariate time series data: a comparative review of performance
Inferring nonlinear and asymmetric causal relationships between multivar...
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Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenge...
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Adaptive Prediction Timing for Electronic Health Records
In realistic scenarios, multivariate timeseries evolve over case-by-case...
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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...
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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...
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DeepClean -- self-supervised artefact rejection for intensive care waveform data using generative deep learning
Waveform physiological data is important in the treatment of critically ...
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Characterising complex healthcare systems using network science: The small world of emergency surgery
Hospitals are complex systems and optimising their function is critical ...
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ICU Disparnumerophobia and Triskaidekaphobia: The 'Irrational Care Unit'?
Whilst evidence-based medicine is the cornerstone of modern practice, it...
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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
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