Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
Semantic similarity measures (SSMs) are widely used in biomedical research
but remain underutilized in pharmacovigilance. This study evaluates six
ontology-based SSMs for clustering MedDRA Preferred Terms (PTs) in drug safety
data. Using the Unified Medical Language System (UMLS), we assess each method's
ability to group PTs around medically meaningful centroids. A high-throughput
framework was developed with a Java API and Python and R interfaces support
large-scale similarity computations. Results show that while path-based methods
perform moderately with F1 scores of 0.36 for WUPALMER and 0.28 for LCH,
intrinsic information content (IC)-based measures, especially INTRINSIC-LIN and
SOKAL, consistently yield better clustering accuracy (F1 score of 0.403).
Validated against expert review and standard MedDRA queries (SMQs), our
findings highlight the promise of IC-based SSMs in enhancing pharmacovigilance
workflows by improving early signal detection and reducing manual review.
