Simon T. Ball

Klarigi identifies compositional and discriminatory ontology classes for groups of biomedical entities, providing semantic explanations that extend existing knowledge.

The paper expands ontology labels to improve clinical text characterization by linking synonyms across ontologies, enhancing semantic similarity for patient annotation.

The paper introduces a heuristic-based negation detection algorithm for clinical text, showing it is fast, accurate, and generalisable compared to existing rule-based methods.

Komenti automates clinical audit by extracting medication data from text, revealing high precision and identifying at-risk patient subgroups.