# Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety

arXiv: https://arxiv.org/abs/2503.20737 · HTML: https://mlsanity.com/paper/2503.20737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials](https://mlsanity.com/paper/2511.18937/) (2025)
- [Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events](https://mlsanity.com/paper/2504.12052/) (2025)

## Abstract (arXiv)

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.