In recent years, information systems have achieved the ability to communicate, exchange data, this is known as functional interoperability. This has been made possible by the use in more developed countries hospitals and healthcare systems of several technologies, including application programming interfaces (APIs) and standardized communications norms and protocols such as TCP/IP, HTTP, FTP, etc.

Semantic interoperability between terminologies, defined as the ability to match the meanings of similar or equivalent terms between different terminologies, to enable clear and precise communication and understanding of information exchanged between IT systems, is still a challenge due to the lack of semantic standards [1] [2] .

Each terminology has been designed to meet a particular need, and a particular use (structuring Clinical Terminology of the medical record, coding information, indexing documents, representing entities in expert systems, input for management and funding. When they claim to represent an invariant concept, it is often untrue whenever information entered for one purpose is reused for another [3] .

There are several lexical-based alignments between the WHO International Classification of Diseases and Health Problems (ICD-10) and the most used in the world clinical terminology named Systematized Nomenclature of Human and Veterinary Medicine—Clinical Terms (SNOMED CT). The most well-known are from SNOMED CT to ICD-10 [4] [5] . In 2010, SNOMED International, formerly known as the International Health Terminology Standards Development Organization (IHTSDO®), signed an institutional agreement with the World Health Organization (WHO). This collaboration aimed to establish a priority set of cross-matches from SNOMED CT to the ICD-10 using a heuristic method to meet the epidemiological, statistical, and administrative needs of SNOMED International members, WHO collaborating centers, and other interested parties. These alignments are available in the SNOMED CT browser under the “refset” tab for certain SNOMED CT concepts [6] .

Several methods exist for implementing semantic interoperability. We can group them into lexical-based. and semantic-based alignment methods. Among these methods, those based on semantics can be distinguished between those that model without using formal logic [7] [8] and more recent methods that use the representation of logical definitions, through the use of standardized formal logic, particularly in the semantic web; this is known as applied ontology [9] .

Under the umbrella of the WHO SNOMED/IHTSDO 2010 agreement a common work WHO IHTSDO has proposed the development of a formal common ontology ICD-11 SCT [10] [11] .

Following these recent developments, we propose an approach for building a formal ontological representation of ICD-11 [12] using first a lexical mapping with UMLS MetaMap between ICD-11 and SNOMED CT codes labels [13] and in a second time a SNOMED formal ontology representation with the SNOMED concept model [14] .

The ICD, now in its 11th version, came into effect in January 2022 for mortality statistics and contains 17,000 unique codes and 120,000 codable terms that can be consulted in the ICD-11 browser [15] . It consists of 26 chapters and two additional sections: 1) an additional section for the assessment of functioning and 2) a section for extension codes.

The WHO provides a table (Excel file) with correspondence between ICD-11 codes and titles and those of ICD-10 (Version of 01/2024) which is still predominantly used in health information systems, whereas ICD-11, the subject of our work, has not yet been adopted by all countries for morbidity [16] . Its implementation will follow a timetable specific to each country.

Our work is on ICD-11 and the method we propose cannot be applied to ICD-10.

We have studied only Chapter 01 (Certain infectious or parasitic diseases). This chapter includes certain conditions caused by pathogenic organisms or micro-organisms such as bacteria, viruses, parasites, or fungi.

It excludes:

It is subdivided into 22 sub-chapters and a title code for infection without precision (IH0Z).

From the Excel file described above, we extracted 1027 codes and titles corresponding to chapter 01 of the ICD-11. We then filtered it by eliminating titles containing the words “other” and “unspecified”, i.e., codes ending in “Y” and “Z”. We finally obtained 651 lines of codes and titles, of which 474 have detailed textual descriptions and 177 do not and have only the label description.

SCT is a medical terminology for healthcare containing 350,000 concepts organized into 19 hierarchies [5] . It is both a reference terminology based on a poly-hierarchical structure of the type and, for certain hierarchies, a formal ontology representation. This formal representation is implemented using a Concept Model [17] and Compositional Grammar rules [18] based on the Ontology Web Language (OWL) of the Semantic Web [19] .

The SCT concept model specifies how to define SCT expressions through semantic categories, definition attributes, and expression constraints according to rules validated in the Machine Readable Concept Model (MRCM) [20] .

Compositional Grammar is a lightweight syntax based on the OWL2 EL logic profile [21] . This syntax allows more representations expressions than the ones validated today in the SCT browser in a way that is both human-readable and machine-readable.

The proposed method consists of two stages:

The method proposed in this work involves lexical alignment with MetaMap [13] and ontological representation with SNOMED concept model representation [17] .

First, we found a 100% lexical match between ICD-11 titles and fully specified names or synonyms of SCT concepts, 94% for a pre-coordinated concept, and 6% for post-coordinated concepts. This result is not surprising given that most healthcare terminologies on a specific subject and in the same language share a large number of terms. During the development of ICD11 SCT was one healthcare terminology taken into account.

The second so-called ontology representation showed a reduced correspondence (53% for SCT concepts FD and 260/651 around 40% for the total) between ICD-11 textual definition or description and SCT concept model for SCT concepts lexically aligned with ICD-11 labels.

This result has two main consequences for the future:

1) It explains the limitations of the mapping between labels or fully defined names of different healthcare terminologies. It is an argument for developing rather a common ontology between them as for ICD11 and SNOMED CT.

2) It shows that the SCT ontology based on an information model and a compositional grammar easily available on the SNOMED browser is under-utilized today for the SCT concept model.

Our work has some limitations:

1) We have shown in this work that a formal ontological representation of a health terminology proposing a detailed textual description of its titles as ICD-11 is possible by using the lexical alignment and ontology representation tools of other terminology systems (UMLS and SCT).

2) The work measures the gap between a lexical alignment and a meaning alignment between different health terminologies.