Francesca Raimondi

Francesca E. D. Raimondi, Tadhg O'Keeffe, Hana Chockler et al.

We describe the results of applying causal discovery methods on the data from a multi-site clinical trial, on the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT). The trial was inconclusive, with no clear benefits consistently shown for the whole cohort. However, there were questions regarding the reliability of the diagnosis and treatment protocol for a geographic subgroup of the cohort. With the inclusion of medical context in the form of domain knowledge, causal discovery is used to demonstrate regional discrepancies and to frame the regional transportability of the results. Furthermore, we show that, globally and especially for some subgroups, the treatment has significant causal effects, thus offering a more refined view of the trial results.

Francesca E. D. Raimondi, Andrew R. Lawrence, Hana Chockler

This paper proposes a method for measuring fairness through equality of effort by applying algorithmic recourse through minimal interventions. Equality of effort is a property that can be quantified at both the individual and the group level. It answers the counterfactual question: what is the minimal cost for a protected individual or the average minimal cost for a protected group of individuals to reverse the outcome computed by an automated system? Algorithmic recourse increases the flexibility and applicability of the notion of equal effort: it overcomes its previous limitations by reconciling multiple treatment variables, introducing feasibility and plausibility constraints, and integrating the actual relative costs of interventions. We extend the existing definition of equality of effort and present an algorithm for its assessment via algorithmic recourse. We validate our approach both on synthetic data and on the German credit dataset.

Javier Bisbal, Julio Sotelo, Maria I Valdés et al.

Deep reinforcement learning (DRL) algorithms have shown robust results in plane reformatting tasks. In these methods, an agent sequentially adjusts the position and orientation of an initial plane towards an objective location. This process allows accurate plane reformatting, without the need for detailed landmarks, which makes it suitable for images with limited contrast and resolution, such as 4D flow MRI. However, current DRL methods require the test dataset to be in the same position and orientation as the training dataset. In this paper, we present a novel technique that utilizes a flexible coordinate system based on the current state, enabling navigation in volumes at any position or orientation. We adopted the Asynchronous Advantage Actor Critic (A3C) algorithm for reinforcement learning, outperforming Deep Q Network (DQN). Experimental results in 4D flow MRI demonstrate improved accuracy in plane reformatting angular and distance errors (6.32 +- 4.15 ° and 3.40 +- 2.75 mm), as well as statistically equivalent flow measurements determined by a plane reformatting process done by an expert (p=0.21). The method's flexibility and adaptability make it a promising candidate for other medical imaging applications beyond 4D flow MRI.