A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons

VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix

Mistakes in medical ontologies: Where do they come from and how can they be detected?

We present the details of a methodology for quality assurance in large medical terminologies and describe three algorithms that can help terminology developers and users to identify potential mistakes. The methodology is based in part on linguistic criteria and in part on logical and ontological principles governing sound classifications. We conclude by outlining the results of applying the methodology in the form of a taxonomy different types of errors and potential errors detected in SNOMED-CT

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

A system for automated lexical mapping

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (leaves 19-20).Merging of clinical systems and medical databases, or aggregation of information from disparate databases, frequently requires a process where vocabularies are compared and similar concepts are mapped. Using a normalization phase followed by a novel alignment stage inspired by DNA sequence alignment methods, automated lexical mapping can map terms from various databases to standard vocabularies such as UMLS (Unified Medical Language System) and SNOMED (the Systematized Nomenclature of Medicine). This automated lexical mapping was evaluated using a real-world database of consultation letters from Children's Hospital Boston. The first phase involved extracting the reason for referral from the consultation letters. The reasons for referral were then mapped to SNOMED. The alignment algorithm was able to map 72% of equivalent concepts through lexical mapping alone. Lexical mapping can facilitate the integration of data from diverse sources and decrease the time and cost required for manual mapping and integration of clinical systems and medical databases.by Jennifer Y. Sun.S.M

SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

Desiderata for domain reference ontologies in biomedicine

AbstractDomain reference ontologies represent knowledge about a particular part of the world in a way that is independent from specific objectives, through a theory of the domain. An example of reference ontology in biomedical informatics is the Foundational Model of Anatomy (FMA), an ontology of anatomy that covers the entire range of macroscopic, microscopic, and subcellular anatomy. The purpose of this paper is to explore how two domain reference ontologies—the FMA and the Chemical Entities of Biological Interest (ChEBI) ontology, can be used (i) to align existing terminologies, (ii) to infer new knowledge in ontologies of more complex entities, and (iii) to manage and help reasoning about individual data. We analyze those kinds of usages of these two domain reference ontologies and suggest desiderata for reference ontologies in biomedicine. While a number of groups and communities have investigated general requirements for ontology design and desiderata for controlled medical vocabularies, we are focusing on application purposes. We suggest five desirable characteristics for reference ontologies: good lexical coverage, good coverage in terms of relations, compatibility with standards, modularity, and ability to represent variation in reality

National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease

STRUCTURAL AND LEXICAL METHODS FOR AUDITING BIOMEDICAL TERMINOLOGIES

Biomedical terminologies serve as knowledge sources for a wide variety of biomedical applications including information extraction and retrieval, data integration and management, and decision support. Quality issues of biomedical terminologies, if not addressed, could affect all downstream applications that use them as knowledge sources. Therefore, Terminology Quality Assurance (TQA) has become an integral part of the terminology management lifecycle. However, identification of potential quality issues is challenging due to the ever-growing size and complexity of biomedical terminologies. It is time-consuming and labor-intensive to manually audit them and hence, automated TQA methods are highly desirable. In this dissertation, systematic and scalable methods to audit biomedical terminologies utilizing their structural as well as lexical information are proposed. Two inference-based methods, two non-lattice-based methods and a deep learning-based method are developed to identify potentially missing hierarchical (or is-a) relations, erroneous is-a relations, and missing concepts in biomedical terminologies including the Gene Ontology (GO), the National Cancer Institute thesaurus (NCIt), and SNOMED CT. In the first inference-based method, the GO concept names are represented using set-of-words model and sequence-of-words model, respectively. Inconsistencies derived between hierarchical linked and unlinked concept pairs are leveraged to detect potentially missing or erroneous is-a relations. The set-of-words model detects a total of 5,359 potential inconsistencies in the 03/28/2017 release of GO and the sequence-of-words model detects 4,959. Domain experts’ evaluation shows that the set-of-words model achieves a precision of 53.78% (128 out of 238) and the sequence-of-words model achieves a precision of 57.55% (122 out of 212) in identifying inconsistencies. In the second inference-based method, a Subsumption-based Sub-term Inference Framework (SSIF) is developed by introducing a novel term-algebra on top of a sequence-based representation of GO concepts. The sequence-based representation utilizes the part of speech of concept names, sub-concepts (concept names appearing inside another concept name), and antonyms appearing in concept names. Three conditional rules (monotonicity, intersection, and sub-concept rules) are developed for backward subsumption inference. Applying SSIF to the 10/03/2018 release of GO suggests 1,938 potentially missing is-a relations. Domain experts’ evaluation of randomly selected 210 potentially missing is-a relations shows that SSIF achieves a precision of 60.61%, 60.49%, and 46.03% for the monotonicity, intersection, and sub-concept rules, respectively. In the first non-lattice-based method, lexical patterns of concepts in Non-Lattice Subgraphs (NLSs: graph fragments with a higher tendency to contain quality issues), are mined to detect potentially missing is-a relations and missing concepts in NCIt. Six lexical patterns: containment, union, intersection, union-intersection, inference-contradiction, and inference-union are leveraged. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. This method identifies 809 NLSs exhibiting these patterns in the 16.12d version of NCIt, achieving a precision of 66% (33 out of 50). In the second non-lattice-based method, enriched lexical attributes from concept ancestors are leveraged to identify potentially missing is-a relations in NLSs. The lexical attributes of a concept are inherited in two ways: from ancestors within the NLS, and from all the ancestors. For a pair of concepts without a hierarchical relation, if the lexical attributes of one concept is a subset of that of the other, a potentially missing is-a relation between the two concepts is suggested. This method identifies a total of 1,022 potentially missing is-a relations in the 19.01d release of NCIt with a precision of 84.44% (76 out of 90) for inheriting lexical attributes from ancestors within the NLS and 89.02% (73 out of 82) for inheriting from all the ancestors. For the non-lattice-based methods, similar NLSs may contain similar quality issues, and thus exhaustive examination of NLSs would involve redundant work. A hybrid method is introduced to identify similar NLSs to avoid redundant analyses. Given an input NLS, a graph isomorphism algorithm is used to obtain its structurally identical NLSs. A similarity score between the input NLS and each of its structurally identical NLSs is computed based on semantic similarity between their corresponding concept names. To compute the similarity between concept names, the concept names are converted to vectors using the Doc2Vec document embedding model and then the cosine similarity of the two vectors is computed. All the structurally identical NLSs with a similarity score above 0.85 is considered to be similar to the input NLS. Applying this method to 10 different structures of NLSs in the 02/12/2018 release of GO reveals that 38.43% of these NLSs have at least one similar NLS. Finally, a deep learning-based method is explored to facilitate the suggestion of missing is-a relations in NCIt and SNOMED CT. Concept pairs exhibiting a containment pattern is the focus here. The problem is framed as a binary classification task, where given a pair of concepts, the deep learning model learns to predict whether the two concepts have an is-a relation or not. Positive training samples are existing is-a relations in the terminology exhibiting containment pattern. Negative training samples are concept-pairs without is-a relations that are also exhibiting containment pattern. A graph neural network model is constructed for this task and trained with subgraphs generated enclosing the pairs of concepts in the samples. To evaluate each model trained by the two terminologies, two evaluation sets are created considering newer releases of each terminology as a partial reference standard. The model trained on NCIt achieves a precision of 0.5, a recall of 0.75, and an F1 score of 0.6. The model trained on SNOMED CT achieves a precision of 0.51, a recall of 0.64 and an F1 score of 0.56