The new research was published this week in the Journal of Biomedical Informatics. The authors are associated with universities and institutions in the United States and Brazil.
The authors’ stated goal was to improve the understanding of terminology used in the study of dietary ingredients and finished supplement formulations with an eye toward matching those with plausible modes of drug-supplement interactions. They did this by “leveraging biomedical natural language processing (NLP) technologies and a DS (dietary supplement) domain terminology. “
Teaching old tool new tricks
The researchers started with an NLP tool called SemRep. which is a “UMLS-based program that extracts three-part propositions, called semantic predications, from sentences in biomedical text.”
UMLS, or Unified Medical Language System, “Integrates and distributes key terminology, classification and coding standards, and associated resources to promote creation of more effective and interoperable biomedical information systems and services, including electronic health records.”
Nomenclature for dietary ingredients has been problematical for decades, especially for botanical ingredients. A key part of the effort was getting the terminology right, so the new NLP tool the researchers developed knew what to look for, and to cut down on spurious returns.
Hundreds of potential new supplement-drug interactions found
They called their new tool SemRepDS, which added in the new dietary supplement terminology to the base SemRep tool. They added in more than 28,000 dietary supplement-specific terms to create their new tool. When applying both tools to studies accessed through PubMed, SemRepDS “returned 158.5% more DS entities and 206.9% more DS relations than SemRep.”
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