Rezarta Islamaj - MeSH Vocabulary Development at the NLM
Channel: Biocuration Conference 2023
Date: 2023-06-21

*Title:* A Framework for Assisting MeSH Vocabulary Development at the National Library of Medicine: Reliably Identifying Literature Containing New Chemical Substances

*Session:* April 25 - Ai And Text Mining 2 - 04

*Abstract:* Medical Subject Headings (MeSH) is a controlled and hierarchically-organized vocabulary produced by the National Library of Medicine (NLM), used for indexing, cataloging, and searching of biomedical and health-related information in PubMed. It consists of more than 30,000 MeSH Headings (descriptors) organized hierarchically in 16 main branches of the MeSH tree, and more than 320,000 supplementary concept records (SCRs) which map to various headings, or branches of the tree. Due to daily advances in biomedicine and related areas, an important goal of the biocuration scientists at NLM is to keep MeSH current. The Chemicals and Drugs tree is the largest main branch in the MeSH tree. It is also one of the most active vocabulary development areas. New chemicals, new synonym terms for existing concepts, and new chemical groups, comprised 62% of the new MeSH requests in 2022. Consequently, from May-December 2022, NLM curators identified 1,140 chemical and protein terms that were not included in MeSH, and after review, created 592 new SCRs, and added 129 new terms/synonyms. To respond to NLMs curators’ need for a tool to assist with rapid identification of new articles containing new chemical names, and relying on our previous success in accurately identifying chemicals in biomedical literature, we developed a framework for assisting the MeSH vocabulary development efforts for chemicals. In March 2022, we started with a selected set of 200 PubMed articles in TeamTat, annotated with the NLM-Chem algorithm for chemicals, which five curators reviewed. Of those, 174 articles contained new chemical terminology, not included in MeSH. These articles were used as the seed for classification training in LitSuggest, which considered all articles published since January 1, 2022, and identified 1,416 that contained chemicals, which were tested for our topic of interest (new chemicals). From this set, 39 articles marked positive from LitSuggest were then ported to TeamTat for manual review. Since March 2022, we have run this framework several times. Each time we re-populate the training set in LitSuggest with articles of interest verified by TeamTat and retrain the classifier. We run the classifier and test the newly published articles in PubMed. We use the NLM-Chem algorithm to mark all chemicals in the LitSuggest identified articles and review them on TeamTat. As a result of this work, we have identified 453 new chemical entries submitted as chemical flags or MeSH requests. Of these, 333 were new chemical substances, 71 terms were new synonyms for existing terms, and 49 were new chemical groups. We find this framework will significantly improve our productivity, and aim to fully integrate it in the indexing production pipeline for new terminology suggestion.