Luke T. Slater

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.

This paper develops a method to use uncurated clinical text phenotypes for differential diagnosis of common diseases using semantic similarity.

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.

The paper identifies and repairs hidden contradictions in biomedical ontologies by combining them and using automated reasoning to find and fix unsatisfiable classes.

The paper develops a framework to extend ontologies using clinical text, improving their ability to characterize and classify medical data.

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