3 research outputs found
Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature
Objective: Our study aimed to construct an exhaustive Complementary and
Integrative Health (CIH) Lexicon (CIHLex) to better represent the often
underrepresented physical and psychological CIH approaches in standard
terminologies. We also intended to apply advanced Natural Language Processing
(NLP) models such as Bidirectional Encoder Representations from Transformers
(BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their
performance against established models like MetaMap and CLAMP. Materials and
Methods: We constructed the CIHLex by integrating various resources, compiling
and integrating data from biomedical literature and relevant knowledge bases.
The Lexicon encompasses 198 unique concepts with 1090 corresponding unique
terms. We matched these concepts to the Unified Medical Language System (UMLS).
Additionally, we developed and utilized BERT models and compared their
efficiency in CIH named entity recognition to that of other models such as
MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in
CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover,
75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as
"Therapeutic or Preventive Procedure." Among the models applied to CIH named
entity recognition, BLUEBERT delivered the highest macro average F1-score of
0.90, surpassing other models. Conclusion: Our CIHLex significantly augments
representation of CIH approaches in biomedical literature. Demonstrating the
utility of advanced NLP models, BERT notably excelled in CIH entity
recognition. These results highlight promising strategies for enhancing
standardization and recognition of CIH terminology in biomedical contexts
Source authenticity in the UMLS – A case study of the Minimal Standard Terminology
AbstractAs the UMLS integrates multiple source vocabularies, the integration process requires that certain adaptation be applied to the source. Our interest is in examining the relationship between the UMLS representation of a source vocabulary and the source vocabulary itself. We investigated the integration of the Minimal Standard Terminology (MST) into the UMLS in order to examine how close its UMLS representation is to the source MST. The MST was conceived as a “minimal” list of terms and structure intended for use within computer systems to facilitate standardized reporting of gastrointestinal endoscopic examinations. Although the MST has an overall schema and implied relationship structure, many of the UMLS integrated MST terms were found to be hierarchically orphaned, and with lateral relationships that do not closely adhere to the source MST. Thus, the MST representation within the UMLS significantly differs from that of the source MST. These representation discrepancies may affect the usability of the MST representation in the UMLS for knowledge acquisition. Furthermore, they pose a problem from the perspective of application developers. While these findings may not necessarily apply to other source terminologies, they highlight the conflict between preservation of authentic concept orientation and the UMLS overall desire to provide fully specified names for all source terms
The Foundational Model of Anatomy Ontology
Anatomy is the structure of biological organisms. The term also denotes the scientific
discipline devoted to the study of anatomical entities and the structural and
developmental relations that obtain among these entities during the lifespan of an
organism. Anatomical entities are the independent continuants of biomedical reality on
which physiological and disease processes depend, and which, in response to etiological
agents, can transform themselves into pathological entities. For these reasons, hard copy
and in silico information resources in virtually all fields of biology and medicine, as a
rule, make extensive reference to anatomical entities. Because of the lack of a
generalizable, computable representation of anatomy, developers of computable
terminologies and ontologies in clinical medicine and biomedical research represented
anatomy from their own more or less divergent viewpoints. The resulting heterogeneity
presents a formidable impediment to correlating human anatomy not only across
computational resources but also with the anatomy of model organisms used in
biomedical experimentation. The Foundational Model of Anatomy (FMA) is being
developed to fill the need for a generalizable anatomy ontology, which can be used and
adapted by any computer-based application that requires anatomical information.
Moreover it is evolving into a standard reference for divergent views of anatomy and a
template for representing the anatomy of animals. A distinction is made between the FMA
ontology as a theory of anatomy and the implementation of this theory as the FMA
artifact. In either sense of the term, the FMA is a spatial-structural ontology of the
entities and relations which together form the phenotypic structure of the human
organism at all biologically salient levels of granularity. Making use of explicit
ontological principles and sound methods, it is designed to be understandable by human
beings and navigable by computers. The FMA’s ontological structure provides for
machine-based inference, enabling powerful computational tools of the future to reason
with biomedical data