Ontology-Enriched Abstract Topic Modelling for Biomedical Trend Discovery: A MeSH-based LDA Framework with BI Visual Analytics

Abstract

The explosive growth of biomedical literature increasingly makes it difficult to identify incipient topics and long-term developments. Citation-based indicators are slow to capture developments and often fail to represent influence. Whilst Latent Dirichlet Allocation (LDA) supports content-based trend discovery, its bag-of-words assumption can overlook synonymy, polysemy, and hierarchical relations that are central to biomedical terminology. Moreover, recent work provides limited direct evidence on how far MeSH-enriched abstract representations improve LDA-based trend analysis when compared against the original abstract text. To fill this gap, an ontology-driven methodology for the pre-topic modelling of article abstracts by enriching them with hierarchical Medical Subject Headings (MeSH) terms is introduced in this study. We evaluate four representations, namely original abstracts, MeSH parent categories, MeSH-matched terms with parents, and a fully enriched representation, using a decade-long Scopus corpus of 624,486 biomedical journal articles (2015–2024). Model performance is assessed using coherence, distinctiveness, perplexity, and stability. The fully enriched representation achieves the strongest overall results (coherence 0.606, distinctiveness 0.996, stability 0.725, perplexity 1782.040) relative to the original abstract baseline. Temporal topic analysis further reveals interpretable patterns, including the COVID-19 surge and longer-term structural shifts such as the rise of machine-learning diagnostics. These findings indicate that MeSH-guided abstract enrichment can strengthen topic quality and support more effective biomedical trend discovery through business-intelligence visual analytics.