Target Terminology Research Articles

TERM NESTS AS A REFLECTION OF THE SYSTEM AND CONCEPTUAL DYNAMICS
 OF THE FIELD OF KNOWLEDGE (ON THE EXAMPLE OF THE EU LANGUAGE POLICY TERMINOLOGY)

The article presents the systematic nature of the terminology of the EU language policy through the construction of term nests. Terminological management techniques (inventory and ordering of terminology); derivational analysis;
 terminological nesting; structural analysis in identifying terminological elements typical for the target subject area are
 used to achieve the goal of the research. The methodology of the research is based on the works of leading scientiststerminologists. Nominations received as a result of processing by the terminological extractors Sketch Engine and AntConc of the research corpus based on the texts of 340 English-language official documents reflecting various aspects of the EU language policy, as well as the terms presented in the glossaries accompanying these documents make up the
 material of the research. 29 nests of all types, namely 4 potential nests, 75 elementary nests, 66 chain nests, 35 fan nests,
 and 110 complex nests, have been constructed. It has been found out that the term nests are an important means to systematize terminology, as they can be used to identify micro-sections of the target subject area and establish links
 between them. It has been deduced that the consistency within the nest is ensured by formal and semantic connections between the terms, and the presence of common terminological elements in different nests reflects the systematic nature
 of the target terminology.

Principles of implementing linguodidactic approaches in the pre-professional level students' terminological vocabulary formation

The phased transition to trilingual education in secondary schools in Kazakhstan led to the need to undertake professionally oriented foreign language teaching, as well as updating curricula and learning materials through the integration of subject content with language learning. The effectiveness of studying comprehensive subjects depends on proficiency in subject-specific target terminology. The article mainly focuses on the principles of implementing linguodidactic approaches in the pre-professional level learners` specialized vocabulary formation, providing insights for teachers according to terminology teaching. This paper presents a short theoretical overview and practical research observations in the field of specialized vocabulary instruction to students. The purpose of the study is to identify an effective implementation practice of linguodidactic approaches in the content of specific lexical acquisition by pre-professional level students of the comprehensive subjects (Physics, Biology, Chemistry, IT/ Computer Science) taught in English. The study uses methods of observation, monitoring, and comparative analysis, as well as literary analysis of secondary education and its pre-professional level requirements in Kazakhstan and ESP methodology of teaching. The article formulates methodological recommendations for effective pre-professional classroom management as an efficient realization of linguodidactic approaches in terminological vocabulary teaching in the target (English) language.

Mapping Korean National Health Insurance Reimbursement Claim Codes for Therapeutic and Surgical Procedures to SNOMED-CT to Facilitate Data Reuse.

South Korea has a public and single-payer system for healthcare services based on fee-for-service payments. The National Health Insurance (NHI) reimbursement claim codes are used by all healthcare providers for reimbursement. This study mapped NHI reimbursement claim codes for therapeutic and surgical procedures to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) to facilitate semantic interoperability and data reuse for research. The Source codes for mapping were 2,500 reimbursement claim codes for therapeutic and surgical procedures such as surgery, endoscopic procedures, and interventional radiology. The target terminology for mapping was the 'Procedure' hierarchy of the international edition of SNOMED-CT released in July 2019. We translated Korean terms into English, clarified their meaning, extracted characteristics of the source codes, and mapped them to pre-coordinated concepts. If a source concept was not mapped to a pre-coordinated concept, we mapped it to a post-coordinated expression. The mapping results were validated internally using dual independent mapping and group discussion by trained terminologists, and by two physicians with experience of SNOMED-CT mapping. Out of 2,500 source codes, 1,298 (51.9%) codes were mapped to pre-coordinated concepts, and 1,202 (48.1%) codes were mapped to post-coordinated expressions. The mapping of the NHI reimbursement claim codes for therapeutic and surgical procedures to SNOMED-CT is expected to support clinical research by facilitating the utilization of health insurance claim data.

Medical concept normalization in French using multilingual terminologies and contextual embeddings

IntroductionConcept normalization is the task of linking terms from textual medical documents to their concept in terminologies such as the UMLS®. Traditional approaches to this problem depend heavily on the coverage of available resources, which poses a problem for languages other than English. ObjectiveWe present a system for concept normalization in French. We consider textual mentions already extracted and labeled by a named entity recognition system, and we classify these mentions with a UMLS concept unique identifier. We take advantage of the multilingual nature of available terminologies and embedding models to improve concept normalization in French without translation nor direct supervision. Materials and methodsWe consider the task as a highly-multiclass classification problem. The terms are encoded with contextualized embeddings and classified via cosine similarity and softmax. A first step uses a subset of the terminology to finetune the embeddings and train the model. A second step adds the entire target terminology, and the model is trained further with hard negative selection and softmax sampling. ResultsOn two corpora from the Quaero FrenchMed benchmark, we show that our approach can lead to good results even with no labeled data at all; and that it outperforms existing supervised methods with labeled data. DiscussionTraining the system with both French and English terms improves by a large margin the performance of the system on a French benchmark, regardless of the way the embeddings were pretrained (French, English, multilingual). Our distantly supervised method can be applied to any kind of documents or medical domain, as it does not require any concept-labeled documents. ConclusionThese experiments pave the way for simpler and more effective multilingual approaches to processing medical texts in languages other than English.

Extending import detection algorithms for concept import from two to three biomedical terminologies

BackgroundWhile enrichment of terminologies can be achieved in different ways, filling gaps in the IS-A hierarchy backbone of a terminology appears especially promising. To avoid difficult manual inspection, we started a research program in 2014, investigating terminology densities, where the comparison of terminologies leads to the algorithmic discovery of potentially missing concepts in a target terminology. While candidate concepts have to be approved for import by an expert, the human effort is greatly reduced by algorithmic generation of candidates. In previous studies, a single source terminology was used with one target terminology.MethodsIn this paper, we are extending the algorithmic detection of “candidate concepts for import” from one source terminology to two source terminologies used in tandem. We show that the combination of two source terminologies relative to one target terminology leads to the discovery of candidate concepts for import that could not be found with the same “reliability” when comparing one source terminology alone to the target terminology. We investigate which triples of UMLS terminologies can be gainfully used for the described purpose and how many candidate concepts can be found for each individual triple of terminologies.ResultsThe analysis revealed a specific configuration of concepts, overlapping two source and one target terminology, for which we coined the name “fire ladder” pattern. The three terminologies in this pattern are tied together by a kind of “transitivity.” We provide a quantitative analysis of the discovered fire ladder patterns and we report on the inter-rater agreement concerning the decision of importing candidate concepts from source terminologies into the target terminology. We algorithmically identified 55 instances of the fire ladder pattern and two domain experts agreed on import for 39 instances. In total, 48 concepts were approved by at least one expert. In addition, 105 import candidate concepts from a single source terminology into the target terminology were also detected, as a “beneficial side-effect” of this method, increasing the cardinality of the result.ConclusionWe showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology.

Military Movies by Hollywood: Assisting ELT in ESP Domain

Teaching English for specific purposes (ESP) is a problem in the educational institutions, because the course books available are not sufficient, up-to-date and interesting enough to teach English for specific purposes. This study aimed to see whether or not the use of military movies was effective in teaching military terminology. The experimental group, a class consisted of 23 students, were given a pre-test and a post-test to see their specific vocabulary competence in military terminology before and after the application of military movies. According to the results of post-test the students in experimental group achieved greater scores than the pre-test results compared to the control group. Control group (N=23) was given the same military terminology content through reading worksheets and the content was studied in a classical way in the classroom. Some of the students in experimental group, however, had some troubles in understanding some of the discourse in the movies. In the light of the findings, the instructors teaching military terminology in English are recommended to make use of the large resource available on movies to teach the target terminology. Moreover, anyone who wants to master the terminology on a certain area himself might exploit ICT and movies as rich resources, which might help students to be autonomous learners. Maintaining and developing overall English competence might also be achieved as the result of watching military movies, so the English teachers should consider this resource as an important asset in their teaching arsenal.

Bridging data models and terminologies to support adverse drug event reporting using EHR data.

This article is part of the Focus Theme of METHODs of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". SALUS project aims at building an interoperability platform and a dedicated toolkit to enable secondary use of electronic health records (EHR) data for post marketing drug surveillance. An important component of this toolkit is a drug-related adverse events (AE) reporting system designed to facilitate and accelerate the reporting process using automatic prepopulation mechanisms. To demonstrate SALUS approach for establishing syntactic and semantic interoperability for AE reporting. Standard (e.g. HL7 CDA-CCD) and proprietary EHR data models are mapped to the E2B(R2) data model via SALUS Common Information Model. Terminology mapping and terminology reasoning services are designed to ensure the automatic conversion of source EHR terminologies (e.g. ICD-9-CM, ICD-10, LOINC or SNOMED-CT) to the target terminology MedDRA which is expected in AE reporting forms. A validated set of terminology mappings is used to ensure the reliability of the reasoning mechanisms. The percentage of data elements of a standard E2B report that can be completed automatically has been estimated for two pilot sites. In the best scenario (i.e. the available fields in the EHR have actually been filled), only 36% (pilot site 1) and 38% (pilot site 2) of E2B data elements remain to be filled manually. In addition, most of these data elements shall not be filled in each report. SALUS platform's interoperability solutions enable partial automation of the AE reporting process, which could contribute to improve current spontaneous reporting practices and reduce under-reporting, which is currently one major obstacle in the process of acquisition of pharmacovigilance data.

Normalizing biomedical terms by minimizing ambiguity and variability

BackgroundOne of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.ResultsWe present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS.ConclusionsThe experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known.

Transformational DT-CNN design from morphological specifications

Morphology provides the algebraic means to specify operations on images. Discrete-time cellular neural networks (DT-CNNs) mechanize the execution of operations on images. The paper first shows the equivalence between morphological functions and DT-CNNs. Then, the argument is extended to the synthesis of optimal DT-CNN structures from complex morphological expressions. It is shown that morphological specifications may be freely derived, to be subsequently transformed and adopted to the needs of a specific target terminology. This process of technology mapping can be automated along the well-trodden path in CAD for microelectronics.