153 citations · 181 across the 9 of their papers we have counts for
9 papers
SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data
Maël Jullien, Marco Valentino, Hannah Frost +3
This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language I…
NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports
Maël Jullien, Marco Valentino, Hannah Frost +3
How can we interpret and retrieve medical evidence to support clinical decisions? Clinical trial reports (CTR) amassed over the years contain indispensable information for the deve…
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…
Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems
Oskar Wysocki, Malina Florea, Donal Landers +1
This paper proposes a novel statistical corpus analysis framework targeted towards the interpretation of Natural Language Processing (NLP) architectural patterns at scale. The prop…
Encoding Explanatory Knowledge for Zero-shot Science Question Answering
Zili Zhou, Marco Valentino, Donal Landers +1
This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mec…