55 citations · 65 across the 5 of their papers we have counts for
5 papers
Informative Missingness to Generate Irregular Clinical Time Series
Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora +4
Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself. Such missingness reflects…
Introducing δ-XAI: a novel sensitivity-based method for local AI explanations
Alessandro De Carlo, Enea Parimbelli, Nicola Melillo +1
Explainable Artificial Intelligence (XAI) is central to the debate on integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into clinical practice. High-per…
Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
Lorenzo Peracchio, Giovanna Nicora, Enea Parimbelli +5
Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in ca…
Tree-based local explanations of machine learning model predictions, AraucanaXAI
Enea Parimbelli, Giovanna Nicora, Szymon Wilk +2
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret. A tradeoff between perf…
The Gene Mover's Distance: Single-cell similarity via Optimal Transport
Riccardo Bellazzi, Andrea Codegoni, Stefano Gualandi +2
This paper introduces the Gene Mover's Distance, a measure of similarity between a pair of cells based on their gene expression profiles obtained via single-cell RNA sequencing. Th…