Semantic Echo Pathways (SEP): Tracing How Medical Language Propagates and Transforms

AbstractWe introduce Semantic Echo Pathways (SEP), a new approach for modeling the cross-domain evolution of medical language. Using continual neural topic models (CoNTM) trained separately on scientific literature, clinical notes, and public health-related data, we track linguistic drift and identify points where concepts change meaning. We propose three novel metrics: Cross-Domain Drift Score, Temporal Echo Lag, and Semantic Mutation Patterns to quantify how medical language travels between the scientific, clinical, and public domain. Applications to evolving concepts such as "long COVID", diagnostic category changes reveal previously undocumented patterns of medical-semantic evolution. Our results bridge computational modeling with the human-centered perspectives of medical humanities, offering clear, domain-aware maps of how medical language shifts across time and domains, and combining quantitative analysis with linguistic and clinical insight.