8 citations · 9 across the 6 of their papers we have counts for
6 papers
Challenges and Opportunities of Shapley values in a Clinical Context
Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz +1
With the adoption of machine learning-based solutions in routine clinical practice, the need for reliable interpretability tools has become pressing. Shapley values provide local e…
PWSHAP: A Path-Wise Explanation Model for Targeted Variables
Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz +2
Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide…
Causal Falsification of Digital Twins
Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet +1
Digital twins are simulation-based models designed to predict how a real-world process will evolve in response to interventions. This modelling paradigm holds substantial promise i…
Generating the right evidence at the right time: Principles of a new class of flexible augmented clinical trial designs
Cornelia Dunger-Baldauf, Rob Hemmings, Frank Bretz +3
The past few years have seen an increasing number of initiatives aimed at integrating information generated outside of confirmatory randomised clinical trials (RCTs) into drug deve…
Causal predictive inference and target trial emulation
Andrew Yiu, Edwin Fong, Stephen Walker +1
Causal inference from observational data can be viewed as a missing data problem arising from a hypothetical population-scale randomized trial matched to the observational study. T…
A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples
Natalia Garcia Martin, Stefano Malacrino, Marta Wojciechowska +8
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to c…