Towards a Terminologia Anatomica Humana.

Unfortunately, the long-awaited revision of the official anatomical nomenclature, the Terminologia Anatomica 2 (TA2), which was issued in 2019 and after a referendum among the Member Societies officially approved by the General Assembly of the International Federation of Associations of Anatomists in 2020, is built on a new version of the Regular Anatomical Terminology (RAT) rules. This breaks with many traditional views of terminology. These changes in the Terminologia Anatomica of 1998 (TA98) met great resistance within many European Anatomical Societies and their members are not willing to use terms following the RAT rules. European anatomy teachers and scientists using traditional Latin in their teaching, textbooks and atlases will keep using the TA98. The German Anatomical Society (Anatomische Gesellschaft) recently announced the usage of the TA2023AG in curricular anatomical media such as textbooks and atlases, based on the TA98 and the Terminologia Neuroanatomica (TNA). We are preparing a more extensive improvement of the TA98, called Terminologia Anatomica Humana (TAH). This project is fully based on the noncontroversial terms of TA98, incorporating the recent digital version (2022) of the TNA from 2017. Further, it is completed with many new terms, including those in TA2, along with their definitions and relevant references, clinical terms, and correcting inconsistencies in the TA98. The TAH is still in process, but many chapters are already freely available at the IFAA Website in Fribourg ( https://ifaa.unifr.ch ) as is the digital version of the TNA.

The Representation of White Matter in the Central Nervous System.

The white matter of the central nervous system (CNS) is difficult to represent in anatomy because it is located predominantly "between" other anatomical entities. In a classic presentation, like a cross section of a brain segment, white matter is present and can be labeled adequately. Several appearances of the same entity are feasible on successive cross section views. The problem is the absence of a global view on long tracts, and more generally, the lack of a comprehensive classification of white matter pathways. Following the recent revision of the Terminologia Anatomica (TA, 1998), in particular the chapter on the nervous system, resulting in the Terminologia Neuroanatomica (TNA, 2017), the authors have developed a new schema for the representation of white matter. In this approach, white matter is directly attached to the CNS, and no longer considered as part of the brain segments. Such a move does not affect the content but redistributes the anatomical entities in a more natural fashion. This paper gives an overall description of this new schema of representation and emphasizes its benefits. The new classification of white matter tracts is developed, selecting the origin as the primary criterion and the type of tract as the secondary criterion.

Ordering by the numbers in anatomy and by letters Too.

Here, new rules of Latin anatomical nomenclature are proposed to deal with cases not covered by existing or other recommended rules. Determiners (e.g., numerals, letters, alphanumeric strings, and Latin names of Greek letters) should follow the noun they specify or limit, just as it is recommended that adjectives should follow the noun they modify. In general, Roman numerals, Latin letters, and Latin names of Greek letters are preferable to Arabic numerals and Greek letters in Latin anatomical terms. It is also noted that the word typus (type) appears to be superfluous and unnecessary in the Latin anatomical nomenclature. Clin. Anat. 30:700-702, 2017. © 2017Wiley Periodicals, Inc.

Human anatomy nomenclature rules for the computer age.

Information systems are increasing in importance in biomedical sciences and medical practice. The nomenclature rules of human anatomy were reviewed for adequacy with respect to modern needs. New rules are proposed here to ensure that each Latin term is uniquely associated with an anatomical entity, as short and simple as possible, and machine-interpretable. Observance of these recommendations will also benefit students and translators of the Latin terms into other languages. Clin. Anat. 30:300-302, 2017. © 2016 Wiley Periodicals, Inc.

Coupling ontology driven semantic representation with multilingual natural language generation for tuning international terminologies.

OBJECTIVES: The importance of clinical communication between providers, consumers and others, as well as the requisite for computer interoperability, strengthens the need for sharing common accepted terminologies. Under the directives of the World Health Organization (WHO), an approach is currently being conducted in Australia to adopt a standardized terminology for medical procedures that is intended to become an international reference. METHOD: In order to achieve such a standard, a collaborative approach is adopted, in line with the successful experiment conducted for the development of the new French coding system CCAM. Different coding centres are involved in setting up a semantic representation of each term using a formal ontological structure expressed through a logic-based representation language. From this language-independent representation, multilingual natural language generation (NLG) is performed to produce noun phrases in various languages that are further compared for consistency with the original terms. RESULTS: Outcomes are presented for the assessment of the International Classification of Health Interventions (ICHI) and its translation into Portuguese. The initial results clearly emphasize the feasibility and cost-effectiveness of the proposed method for handling both a different classification and an additional language. CONCLUSION: NLG tools, based on ontology driven semantic representation, facilitate the discovery of ambiguous and inconsistent terms, and, as such, should be promoted for establishing coherent international terminologies.

Reconciliation of ontology and terminology to cope with linguistics.

OBJECTIVES: To discuss the relationships between ontologies, terminologies and language in the context of Natural Language Processing (NLP) applications in order to show the negative consequences of confusing them. METHODS: The viewpoints of the terminologist and (computational) linguist are developed separately, and then compared, leading to the presentation of reconciliation among these points of view, with consideration of the role of the ontologist. RESULTS: In order to encourage appropriate usage of terminologies, guidelines are presented advocating the simultaneous publication of pragmatic vocabularies supported by terminological material based on adequate ontological analysis. CONCLUSIONS: Ontologies, terminologies and natural languages each have their own purpose. Ontologies support machine understanding, natural languages support human communication, and terminologies should form the bridge between them. Therefore, future terminology standards should be based on sound ontology and do justice to the diversities in natural languages. Moreover, they should support local vocabularies, in order to be easily adaptable to local needs and practices.

Defining and relating biomedical terms: towards a cross-language morphosemantics-based system.

This paper addresses the issue of how semantic information can be automatically assigned to compound terms, i.e. both a definition and a set of semantic relations. This is particularly crucial when elaborating multilingual databases and when developing cross-language information retrieval systems. The paper shows how morphosemantics can contribute in the constitution of multilingual lexical networks in biomedical corpora. It presents a system capable of labelling terms with morphologically related words, i.e. providing them with a definition, and grouping them according to synonymy, hyponymy and proximity relations. The approach requires the interaction of three techniques: (1) a language-specific morphosemantic parser, (2) a multilingual table defining basic relations between word roots and (3) a set of language-independent rules to draw up the list of related terms. This approach has been fully implemented for French, on an about 29,000 terms biomedical lexicon, resulting to more than 3000 lexical families. A validation of the results against a manually annotated file by experts of the domain is presented, followed by a discussion of our method.

An Ontology driven collaborative development for biomedical terminologies: from the French CCAM to the Australian ICHI coding system.

The CCAM French coding system of clinical procedures was developed between 1994 and 2004 using, in parallel, a traditional domain expert's consensus method on one hand, and advanced methodologies of ontology driven semantic representation and multilingual generation on the other hand. These advanced methodologies were applied under the framework of an European Union collaborative research project named GALEN and produced a new generation of biomedical terminology. Following the interest in several countries and in WHO, the GALEN network has tested the application of the ontology driven tools to the existing reduced Australian ICHI coding system for interventions presently under investigation by WHO to check its ability and appropriateness to become the reference international coding system for procedures. The initial results are presented and discussed in terms of feasibility and quality assurance for sharing and maintaining consistent medical knowledge and allowing diversity in linguistic expressiveness of end users.

Recent advances in natural language processing for biomedical applications.

We survey a set a recent advances in natural language processing applied to biomedical applications, which were presented in Geneva, Switzerland, in 2004 at an international workshop. While text mining applied to molecular biology and biomedical literature can report several interesting achievements, we observe that studies applied to clinical contents are still rare. In general, we argue that clinical corpora, including electronic patient records, must be made available to fill the gap between bioinformatics and medical informatics.

Health search engine with e-document analysis for reliable search results.

OBJECTIVE: After a review of the existing practical solution available to the citizen to retrieve eHealth document, the paper describes an original specialized search engine WRAPIN. METHOD: WRAPIN uses advanced cross lingual information retrieval technologies to check information quality by synthesizing medical concepts, conclusions and references contained in the health literature, to identify accurate, relevant sources. Thanks to MeSH terminology [1] (Medical Subject Headings from the U.S. National Library of Medicine) and advanced approaches such as conclusion extraction from structured document, reformulation of the query, WRAPIN offers to the user a privileged access to navigate through multilingual documents without language or medical prerequisites. RESULTS: The results of an evaluation conducted on the WRAPIN prototype show that results of the WRAPIN search engine are perceived as informative 65% (59% for a general-purpose search engine), reliable and trustworthy 72% (41% for the other engine) by users. But it leaves room for improvement such as the increase of database coverage, the explanation of the original functionalities and an audience adaptability. CONCLUSION: Thanks to evaluation outcomes, WRAPIN is now in exploitation on the HON web site (http://www.healthonnet.org), free of charge. Intended to the citizen it is a good alternative to general-purpose search engines when the user looks up trustworthy health and medical information or wants to check automatically a doubtful content of a Web page.

Amplification of Terminologia anatomica by French language terms using Latin terms matching algorithm: a prototype for other language.

OBJECTIVE: Terminologia anatomica is the new standard in anatomical terminology. This terminology is available only in Latin and English and its worldwide adoption is subject to the addition of terms from others languages. On the other hand, Nomina anatomica, the previous standard, has been widely translated. Aim of this work was to append foreign terms to Terminologia by using similarity-matching algorithm between its Latin terms and those from Nomina. METHODS: A semi-automatic matching of Latin terms from Terminologia with those of Nomina was performed using a string-to-string distance algorithm and manual assessment. We used a French-Latin version of Nomina together with Terminologia and we suggested French terms for Terminologia. Coverage was evaluated by the number of exact and approximate matches. A target of 78% was set due to the higher number of terms in Terminologia compared to Nomina. Relevance was estimated by manually comparing the meanings of the English and French terms related to the same Latin term. The question was whether they refer to the same anatomical structure. RESULTS: Exact or approximate matches were found for 5982 terms (76.5%) of Terminologia. Our results indicated that more than 75% of the terms from Terminologia came from Nomina, most of them were left unchanged and all were used with the same meaning. CONCLUSION: This method produces relevant results, reaching our 78% target. The method is based only on Latin terms and can be used for other languages. We consider this work as a starting point for adding terms to other knowledge sources, such as the foundational model of anatomy or the Unified Medical Language System (UMLS).

Methodology to ease the construction of a terminology of problems.

INTRODUCTION: Problem lists summarize an aspect of the patient's medical history and provide an important way to implement entry points for clinical pathways and guideline-oriented care. However, in order to automate processes based on problem lists, the use of controlled vocabularies is required. We developed a methodology to extract a collection of standardized problem-related terms from medical documents entered in free text by physicians. METHODS: We extracted a corpus of sentences describing problems from a randomized selection of admission notes collected at the University Hospitals of Geneva. Theses sentences underwent manual and automatic normalization processes, and a statistical clustering, in order to build a set of terms. RESULTS: We obtained 17,805 sentences from 5000 admission notes. We refined them into 1546 terms, 88.6% of which could be related to a relevant problem statement. DISCUSSION: A clinically relevant problems terminology was derived from clinical admission notes in free-text using a few methodical steps with a reasonable investment of human resources. Such an approach will ease the development and the use of problem lists better suited to user needs.

Towards a multilingual medical lexicon.

We present results of the collaboration of a multinational team of researchers from (computational) linguistics, medicine, and medical informatics with the goal of building a multilingual medical lexicon with high coverage and complete morpho-syntactic information. Monolingual lexical resources were collected and subsequently mapped between languages using a morpho-semantic term normalization engine, which captures intra- as well as interlingual synonymy relationships on the level of subwords.

Desiderata for representing anatomical knowledge.

The general problem of knowledge representation for gross anatomy supporting both computers and human is rarely globally solved. Partial solutions are flourishing, but the actual and potential users are left with a lack of satisfaction and uncomfortable feeling of incompleteness. Moreover, these solutions are not ready for a sound evolution and are at risk to disappear at any moment by default of adequate maintenance. In addition, the problem is complicated by the fact that any solutions should be relevant for Natural Language Processing applications in a multilingual environment.This paper tackles with this problem and defines the basic steps for a proper knowledge representation scheme. Taking the subdomain of gross anatomy, it shows how each step has been solved and what performances and benefits are expected by such a solution. A discussion is done on the way to interface from a common source for both computers and humans.

Towards a multilingual version of terminologia anatomica.

OBJECTIVE: Terminologia Anatomica (TA) is the new standard in anatomical terminology. This terminology is available only in Latin and English and its worldwide adoption is subdued to the addition of terms from others languages. On the other hand Nomina Anatomica (NA), the previous standard, has been widely translated. Aim of this work was to append foreign terms to TA by using similarity matching algorithm between its Latin terms and those from NA. METHODS: A semi-automatic matching of Latin terms from TA with those of NA was performed using a string-to-string distance algorithm and manual assessment. We used a French - Latin version of NA together with TA and we suggested French terms for TA. Coverage was evaluated by the number of exact and approximate matches. A target of 80% was set due to the superior number of terms in TA compared to NA. Relevance was estimated by manually comparing the meanings of the English and French terms related to the same Latin term. The question was whether they refer to the same anatomical structure. RESULTS: Exact or approximate matches were found for 5,982 terms (76.5%) of TA. Our results outlined that more than 75% of the terms from TA came from NA, most of them were left unchanged and all were used with the same meaning. CONCLUSION: This method produces relevant results, reaching our 80% target. The method is based only on Latin terms and can be used for other languages and for others terminologies including Latin terms.

Predicting Lexical Relations between Biomedical Terms: towards a Multilingual Morphosemantics-based System.

This paper addresses the issue of how semantic information can be automatically assigned to compound terms, i.e. both a definition and a set of semantic relations. This issue is particularly crucial when elaborating multilingual databases and when developing cross-language information retrieval systems. The paper shows how morpho-semantics can contribute in the constitution of multilingual lexical networks in biomedical corpora. It presents a system capable of labelling terms with morphologically related words, i.e. providing them with a definition, and grouping them according to synonymy, hyponymy and proximity relations. The approach requires the interaction of three techniques: (1) a la morphosemantic parser, (2) a multilingual table defining basic relations between word roots, and (3) a set of language-independant rules to draw up the list of related terms. This approach has been fully implemented for French, on an about 29,000 terms biomedical lexicon, resulting to more than 3,000 lexical families.

Extracting key sentences with latent argumentative structuring.

PROBLEM: Key word assignment has been largely used in MEDLINE to provide an indicative "gist" of the content of articles. Abstracts are also used for this purpose. However with usually more than 300 words, abstracts can still be regarded as long documents; therefore we design a system to select a unique key sentence. This key sentence must be indicative of the article's content and we assume that abstract's conclusions are good candidates. We design and assess the performance of an automatic key sentence selector, which classifies sentences into 4 argumentative moves: PURPOSE, METHODS, RESULTS and CONCLUSION. METHODS: We rely on Bayesian classifiers trained on automatically acquired data. Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices. We also explore the use of simple heuristics to take the position of sentences into account. Recall, precision and F-scores are computed for the CONCLUSION class. For the CONCLUSION class, the F-score reaches 84%. Automatic argumentative classification is feasible on MEDLINE abstracts and should help user navigation in such repositories.

UMLF: a unified medical lexicon for French.

Medical Informatics has a constant need for basic medical language processing tasks, e.g. for coding into controlled vocabularies, free text indexing and information retrieval. Most of these tasks involve term matching and rely on lexical resources: lists of words with attached information, including inflected forms and derived words, etc. Such resources are publicly available for the English language with the UMLS Specialist Lexicon, but not in other languages. For the French language, several teams have worked on the subject and built local lexical resources. The goal of the present work is to pool and unify these resources and to add extensively to them by exploiting medical terminologies and corpora, resulting in a unified medical lexicon for French (UMLF). This paper exposes the issues raised by such an objective, describes the methods on which the project relies and illustrates them with experimental results.

Coping with the variability of medical terms.

OBJECTIVES: To cope with medical terms, which present a high variability of expression through a single natural language, in the sense that any term may be reformulated in hundred of different ways. METHODS: A typology of term variants is presented as a systematic approach in order to favour the implementation of an exhaustive solution. Then, an algorithm able to handle all variants is designed. RESULTS: Using MetaMap, single terms are analyzed with a success rate varying between 68 and 88 %; the algorithm presented in this paper improves this situation. CONCLUSIONS: This experience shows that a semantic driven method, based on a thesaurus, provides a satisfactory solution to the problem of variability of a single term. The presented typology is representative of most variants in a language.

A natural language based search engine for ICD10 diagnosis encoding.

We have developed a multiple step process for implementing an ICD10 search engine. The complexity of the task has been shown and we recommend collecting adequate expertise before starting any implementation. Underestimation of the expert time and inadequate data resources are probable reasons for failure. We also claim that when all conditions are met in term of resource and availability of the expertise, the benefits of a responsive ICD10 search engine will be present and the investment will be successful.