5 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
Causal Spatio-Temporal Neural Distance Fields for Counterfactual Cardiac Anatomy
Mathias Lowes, Kurt Butler, Kristine Aavild Sørensen +4
Counterfactual cardiac anatomy can support risk modelling and intervention planning, by predicting how a patient's heart would change under altered clinical conditions. However, cu…
Large Language Models Extend Known Research But Do Not Create New Research Directions
Zhihua Liu, Kurt Butler, Damian Machlanski +2
The success of autonomous research systems relies on the quality of the scientific hypotheses they generate. Recent studies report that human reviewers often rate LLM-generated ide…
Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
Junyu Yan, Damian Machlanski, Kurt Butler +4
Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when te…
A Causal Framework for Mitigating Data Shifts in Healthcare
Kurt Butler, Stephanie Riley, Damian Machlanski +13
Developing predictive models that perform reliably across diverse patient populations and heterogeneous environments is a core aim of medical research. However, generalization is o…
Rethinking Chronological Causal Discovery with Signal Processing
Kurt Butler, Damian Machlanski, Panagiotis Dimitrakopoulos +1
Causal discovery problems use a set of observations to deduce causality between variables in the real world, typically to answer questions about biological or physical systems. The…