Search Results for author:
Found 4 papers, 4 papers with code
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients
Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data.
Uncertainty estimation for classification and risk prediction on medical tabular data
In a data-scarce field such as healthcare, where models often deliver predictions on patients with rare conditions, the ability to measure the uncertainty of a model's prediction could potentially lead to improved effectiveness of decision support tools and increased user trust.
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
In this work we show how Bayesian modelling and the predictive uncertainty that it provides can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting.
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks
Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance.
