MeSHelper: Predicting the evolution of Medical Subject Headings based on knowledge graph dynamics

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary widely used in biomedical knowledge systems. We propose a novel framework, termed MeSHelper, that employs a dynamic knowledge graph to predict whether the MeSH main headings (MHs) will evolve while also predicting their corresponding revision type. We parsed the whole PubMed database and all MeSH releases to construct a dynamic semantic tree (DST) and a dynamic knowledge network (DKN) to characterise the evolutionary patterns of MHs and create prediction models. Our results show that DST-related features play a major role in predicting whether the MHs will be revised. Our prediction performance achieved an F1 score of 92.07%. Both DST- and DKN-related features play a crucial role in predicting which types of MHs will evolve. The prediction performance achieved a Macro-F1 score of 72.15%, a Micro-F1 score of 84.09% and a Weighted-F1 score of 84.55%. The findings of this work aid both in constructing an automatic update model for domain thesauruses and in detecting evolutionary trends of the domain knowledge system.