Understanding Terminological Systems II: Experience with Conceptual and Formal Representation of Structure

Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11

Purpose

Widespread electronic health record adoption has generated a large volume of data and emphasized the need for standardized terminology to describe clinical concepts. Here, we undertook a systematic concept coverage analysis to determine the representation of clinical concepts in ophthalmic infection and ophthalmic trauma among standardized medical terminologies, including the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), the International Classification of Diseases (ICD) version 10 with clinical modifications (ICD-10-CM), and ICD version 11 (ICD-11).

Design

Extraction of concepts related to ophthalmic infection and ophthalmic trauma and structured search in terminology browsers.

Data Sources

The American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser.

Methods

Concepts pertaining to ophthalmic infection and ophthalmic trauma were extracted from the 2022 BCSC free text and index terms. We searched terminology browsers to identify corresponding codes and classified the extent of semantic alignment as equal, wide, narrow, or unmatched in each terminology. The overlap of equal concepts in each terminology was represented in a Venn diagram.

Main Outcome Measures

Proportions of clinical concepts with corresponding codes at various levels of semantic alignment.

Results

A total of 443 concepts were identified: 304 concepts related to ophthalmic infection and 139 concepts related to ophthalmic trauma. The SNOMED-CT had the highest proportion of equal coverage, with 82.0% (249 of 304) among concepts related to ophthalmic infection and 82.0% (115 of 139) among concepts related to ophthalmic trauma. Across all concepts, 28% (124 of 443) were classified as equal in ICD-10-CM and 52.8% (234 of 443) were classified as equal in ICD-11.

Conclusions

The SNOMED-CT had significantly better semantic alignment than ICD-10-CM and ICD-11 for ophthalmic infections and ophthalmic trauma. This demonstrates opportunity for continuing advancement of representation of ophthalmic concepts in standardized medical terminologies.

Soarian™ – Workflow Management Applied for Health Care

Objectives: To describe and comment on functionality and architecture of the software product Soarian™ developed by Siemens, to identify key differentiators to related products, and to comment on predecessor systems and beta versions. This has been done in the framework of a conference on health information systems of the IMIA.

Methods: Analyzing existing literature. Site visit of a predecessor system at Haukeland Sykehus, Bergen. Pilot of a beta version at the Erlangen University Medical Center, elaborating on major characteristics in discussion rounds.

Results: Soarian is a functional comprehensive, clinically oriented software product to support health care processes and to be used for health care professional workstations. It is a software product, designed and written completely new. Three major key differentiators were identified in comparison to related software products: Soarian’s workflow engine, its embedded analytics, and its ‘smart’ user interface. The targeted reduced installation time is stated to be 12 months or less.

Conclusions: Soarian has good chances to become one of the major software products for health care professional workstations in the international market to support patient-centered, shared care. Its global design may help to better support and maintain national or language specific versions. The first installations of Soarian will be critical, as they will show how the system will be accepted. To use such software products efficiently, organizational aspects within hospitals as well as between health care institutions have to be considered, e.g. strategic IT planning.

A global, comprehensive review of literature related to paper recycling: A pressing need for a uniform system of terms and definitions

A global, comprehensive review of terms and definitions related to paper recycling was conducted in this article. Terms and definitions related to paper recycling have varied in the course of time. Different terms and different definitions for the same thing are being used in different geographical regions and by different organizations. Definitions are different based on varying conceptions of waste paper as a raw material. Definitions of how to make various calculations related to paper recycling activity are inconsistent. Even such fundamental basic definitions like how to calculate recycling rate and paper consumption are not uniform. It could be concluded that there is no uniform system of terms and definitions related to paper recycling and the implications of this deficiency are profound. For example, it is difficult to reliably compare with each other statistics from different times and from different geographical regions. It is not possible to measure if targets for recycling activities are met if the terms describing the targets are not uniformly defined. In cases of reporting data for recycling targets, the lack of uniform terminology can, for example, impede the necessary transparency between different stakeholders and may allow for deception. The authors conclude there is a pressing need to develop a uniform system of terms and definition for terms related to paper recycling.

NOBLE - Flexible concept recognition for large-scale biomedical natural language processing

Background

Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus.

NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system’s matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator.

Results

We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE’s performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems.

Conclusion

NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.

Standardized data collection to build prediction models in oncology: a prototype for rectal cancer

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.

Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents

OBJECTIVE

To evaluate the effectiveness of a lexico-syntactic pattern (LSP) matching method for ontology enrichment using clinical documents.

METHODS

Two domains were separately studied using the same methodology. We used radiology documents to enrich RadLex and pathology documents to enrich National Cancer Institute Thesaurus (NCIT). Several known LSPs were used for semantic knowledge extraction. We first retrieved all sentences that contained LSPs across two large clinical repositories, and examined the frequency of the LSPs. From this set, we randomly sampled LSP instances which were examined by human judges. We used a two-step method to determine the utility of these patterns for enrichment. In the first step, domain experts annotated medically meaningful terms (MMTs) from each sentence within the LSP. In the second step, RadLex and NCIT curators evaluated how many of these MMTs could be added to the resource. To quantify the utility of this LSP method, we defined two evaluation metrics: suggestion rate (SR) and acceptance rate (AR). We used these measures to estimate the yield of concepts and relationships, for each of the two domains.

RESULTS

For NCIT, the concept SR was 24%, and the relationship SR was 65%. The concept AR was 21%, and the relationship AR was 14%. For RadLex, the concept SR was 37%, and the relationship SR was 55%. The concept AR was 11%, and the relationship AR was 44%.

CONCLUSION

The LSP matching method is an effective method for concept and concept relationship discovery in biomedical domains.

Alignment of the UMLS semantic network with BioTop: methodology and assessment

MOTIVATION

For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop.

METHODS

The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability.

RESULTS

The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations.

AVAILABILITY

BioTop, the OWL DL representation of the UMLS SN, and the mapping ontology are available at http://www.purl.org/biotop/.

Topological analysis of large-scale biomedical terminology structures

OBJECTIVE

To characterize global structural features of large-scale biomedical terminologies using currently emerging statistical approaches.

DESIGN

Given rapid growth of terminologies, this research was designed to address scalability. We selected 16 terminologies covering a variety of domains from the UMLS Metathesaurus, a collection of terminological systems. Each was modeled as a network in which nodes were atomic concepts and links were relationships asserted by the source vocabulary. For comparison against each terminology we created three random networks of equivalent size and density.

MEASUREMENTS

Average node degree, node degree distribution, clustering coefficient, average path length.

RESULTS

Eight of 16 terminologies exhibited the small-world characteristics of a short average path length and strong local clustering. An overlapping subset of nine exhibited a power law distribution in node degrees, indicative of a scale-free architecture. We attribute these features to specific design constraints. Constraints on node connectivity, common in more synthetic classification systems, localize the effects of changes and deletions. In contrast, small-world and scale-free features, common in comprehensive medical terminologies, promote flexible navigation and less restrictive organic-like growth.

CONCLUSION

While thought of as synthetic, grid-like structures, some controlled terminologies are structurally indistinguishable from natural language networks. This paradoxical result suggests that terminology structure is shaped not only by formal logic-based semantics, but by rules analogous to those that govern social networks and biological systems. Graph theoretic modeling shows early promise as a framework for describing terminology structure. Deeper understanding of these techniques may inform the development of scalable terminologies and ontologies.

Developing the ontological foundations of a terminological system for end-stage diseases, organ failure, dialysis and transplantation

The Etablissement français des Greffes (EfG) is a state agency dealing with Public Health issues related to organ, tissue and cell transplantation in France. The evaluation of organ retrieval and transplantation activities, one of its missions, is supported by a national information system (EfG-IS). The EfG-IS is moving towards a new n-tier architecture comprising a terminology server for end-stage diseases, organ failure, dialysis and transplantation (EfG-TS). Following a preliminary audit of the existing coding system and in order to facilitate data recording, to improve the quality of information, to assume compatibility with terminological existing standards and to allow semantic interoperability with other local, national or international registries, a specific work has been conducted on the thesauri to integrate within the EfG-TS. In this paper focusing on the server's content rather than the container, we report first the functional and cognitive requirements that resulted from the preliminary audit. We then describe the methodological approach used to build the terminological server on "sound ontological foundations". We performed the semantic analysis of existing medical terms to set up disease description frame-like structures. These diseases description frames consist of a limited set of nosological discriminating slots such as etiology, semiology, pathology, evolution and associated diseases. Each relevant medical term is thus associated to a concept defined and inserted within a hierarchy according to disease description frame resulting from the semantic analysis. Last, because this terminological server is shared by various transplant and dialysis centers to record patient data at different time point, contextualization of terms appeared as one of the functional requirements. We will also point out various contexts for medical terms and how they have been taken into account.