153 citations · 181 across the 9 of their papers we have counts for
5 papers · 1 filter
Active entailment encoding for explanation tree construction using parsimonious generation of hard negatives
Alex Bogatu, Zili Zhou, Dónal Landers +1
Entailment trees have been proposed to simulate the human reasoning process of explanation generation in the context of open--domain textual question answering. However, in practic…
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Magdalena Wysocka, Oskar Wysocki, Marie Zufferey +2
There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will delive…
Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy
Alex Bogatu, Magdalena Wysocka, Oskar Wysocki +4
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising res…
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
Oskar Wysocki, Jessica Katharine Davies, Markel Vigo +4
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study revealed a more nuanced role for…
Transformers and the representation of biomedical background knowledge
Oskar Wysocki, Zili Zhou, Paul O'Regan +4
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the…