Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

By Olivier Bodenreider. Yearbook of Medical Informatics, 2008

409 citations recorded by the source corpus.

Summary Objectives To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. MethodsBiomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed. Geissbuhler A, Kulikowski C, editors. IMIA Year book of Medical Informatics 2008.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ce10f958a90455a05404747c4c740b758acede3e

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Citations in the corpus

Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users (2012, methodology): “This ontology also aims to facilitate semantic interoperability with other ontologies, terminologies, and databases, and to provide decision support [Bodenreider, 2008].”
Ontological realism: A methodology for coordinated evolution of scientific ontologies (2010, neutral): “Important users include, in addition to those listed in Section 1.9, also Cornelius Rosse, Melissa Haendel, Onard Mejino in the FMA and CARO anatomy ontology projects (Rosse & Mejino, 2007; Haendel et al., 2008); Melanie Courtot, Philippe Rocca-Serra, Susanna-Assunta Sansone, Chris Stoeckert, and th”
Wikidata: A large-scale collaborative ontological medical database (2019, background): “…and “risk factor”) should create a further link between medical concepts to give biomedical computer programs the ability to clinically analyze provided inputs (such as Electronic Health Records, IoT data, and X-ray images) as well as the ability of returning needed outputs (Bodenreider, 2008).”
Concept annotation in the CRAFT corpus (2012, background): “Development of formal ontologies for the representation of domain-specific knowledge has also made substantial progress [6].”
A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases (2015, background): “The process of identifying medical concepts and resolving them to a fixed vocabulary from arbitrary text is not a trivial problem (Bodenreider, 2008).”
K-BERT: Enabling Language Representation with Knowledge Graph (2019, methodology): “e knowledge graph (KG), which was called ontology in early research, serves as a good solution. As knowledge reﬁned into a structured form, KGs over many ﬁelds have been constructed, e.g., SNOMED-CT (Bodenreider 2008) in medical ﬁeld, HowNet (Dong, Dong, and Hao 2006) in Chinese conception. If KG ca”