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Terminology integration and inconsistency identification of adverse event terminology for Japanese medical devices using SPARQL

Abstract

Background

For standardization of terms in the reports of medical device adverse events, 89 Japanese medical device adverse event terminologies were published in March 2015. The 89 terminologies were developed independently by 13 industry associations, suggesting that there may be inconsistencies among the terms proposed. The purpose of this study was to integrate the 89 sets of terminologies and evaluate inconsistencies among them using SPARQL.

Methods

In order to evaluate the inconsistencies among the integrated terminology, the following six items were evaluated: (1) whether the two-layer structure between category term and preferred term is consistent, (2) whether synonyms of a preferred term are involved. Reversing the layer-category order of matching was also performed, (3) whether each preferred term is subordinate to only one category term, (4) whether the definitions of terms are uniquely determined, (5) whether CDRH-NCIt terms corresponding to preferred terms are uniquely determined, (6) whether a term in a medical device problem is used for patient problems.

Results

About 60% of the total number of duplicated terms were found. This is because industry associations that created multiple terminologies adopted the same terms in terminologies of similar medical device groups. In the case that all terms with the same spelling have the same concept, efficient integration can be achieved automatically using RDF. Furthermore, we evaluated six matters of inconsistency in this study, terms that need to be reviewed accounted for about 10% or less than 10% in each item.

Conclusions

The RDF and SPARQL were useful tools to explore inconsistencies of hierarchies, definition statements, and synonyms when integrating terminolgy by term notation, and these had the advantage of reducing the physical and time burden.

Peer Review reports

Background

It is important to collect and analyze reports of adverse events of medical devices to be able to improve the safety of medical devices. To achieve this, and especially to identify causes of adverse events and patient problems, it is necessary to use statistical analysis to standardize the terms used in reports, to establish information that will be helpful to improve the safety of medical devices.

For the standardization of terms, The Ministry of Health, Labour and Welfare of Japan announced official terms for use with Japanese medical devices in the event of adverse events in March 2015 [1]. These include 89 terminology items for groups of Japanese medical device nomenclatures developed by 13 industry associations (Table 1) who are members of The Japan Federation of Medical Devices Associations (JFMDA), and these 89 terminology items are collectively named the JFMDA terminology. Internationally, The International Medical Device Regulators Forum (IMDRF) was conceived to accelerate international medical device regulatory harmonization and convergence in 2011 [2].

Table 1 List of product group names (Excerpt)

At the same time, the 13 Japan industry associations independently each developed the terminologies using a bottom-up approach by gathering the terms used regularly in medical facilities to facilitate communication between medical staff and medical device manufacturers. Therefore, there may be inconsistencies among these terminoloigy items. A previous study stated that inconsistencies in terminology have a negative impact on effective communication and making sense of research findings, integrating studies, and building an integrated theory [3]. Conclusively, any heterogeneity of the JFMDA terminology may lead to inaccurate report analysis and interpretations of results. This makes it necessary to perform an accurate and detailed auditing of the terminology. Since there are few experts for auditing terminology in the JFMDA, it is to provide a continuous cycle including the automatic creation of inconsistency lists and considerations for terminology improvement.

In addition, several methods have been proposed for auditing inconsistencies in terminology. Van Damme et al. [4] evaluated the correctness and completeness of the ontology by referring to templates generated by lexical and clustering techniques. Bodenreider [5] describes the causes and solutions of Circular Hierarchical Relationships in UMLS. Zheng et al. [6] identified the missing is-a relationship using a transformation-based method to replace noun chunks in a concept name with more general concept names, and Cimino [7] visually detected inconsistent relations. However, various methods for auditing have been proposed, it is desirable to be able to automatically detect inconsistencies based on a policy of terminology creation to continuously maintain the terminology in a situation where there are few terminology specialists. In order to manage JFMDA terminology efficiently in that kind of situations, it is useful to describe the structure of the terminology by descriptive logic and detect inconsistencies using inference. Hoehndorf et al. [8] generated a relation template and detected inconsistencies of biomedical ontologies by inference. Jiao et al. [9] used the Resource Description Framework (RDF), which is a language for description logic to identify errors such as syntax errors and logical inconsistencies. Description logic has the advantage of maintaining the consistency of the structure in the terminology, and RDF is the standard model for data exchange on the Web and has been developed and agreed by the World Wide Web Consortium [10].

Our strategy was to integrate all terminology items and identify inconsistencies in hierarchical structures with an automatic approach. Because there are about 3500 terms used with problems generated by medical devices, manual verification requires considerable effort. It is necessary to develop tools to map these as described by RDF and identify inconsistencies using query templates created based on the policy of JFMDA terminology. In a previous study, we integrated 89 terminologies and determined their respective inconsistencies and focused on the terms expressed [11]. In this study, we focus on evaluating inconsistencies in a hierarchy structure and relationships between terms for the elaboration of the terminology. The purpose of this study was to enable evaluation of JFMDA terminology inconsistencies automatically using RDF and its query, SPARQL Protocol and RDF Query Language (SPARQL), from the point of view of hierarchy structure and relationships among terms.

Methods

JFMDA terminology

The JFMDA terminology 1st edition presented 89 terminology sets. Each terminology set includes the names of groups of terms used in Japanese medical device nomenclature, the industry associations that created the terminology, and the Japan medical device nomenclature which the terminology applies to. “Medical device problems” and “patient problems” in the terminology consists of “preferred terms” described as the detail of adverse event and patient problem and “category terms,” which categorized preferred terms into two hierarchies. Each “category term” has one or two or more preferred terms, and each “preferred term” has one or two or more synonyms. In addition, a preferred term is mapped a term for the centers for devices and radiological health and the national cancer institute's thesaurus (CDRH-NCIt) terminology [12] (Fig. 1). Table 1 shows examples of product group names. Incidentally the latest version is 4th edition and it has four categories of terms: “medical device problems”, “patient problems”, “investigation method, findings and conclusion” and “medical device component/accessary.” In addition, a preferred term is mapped as a term for the IMDRF code replacing the CDRH-NCIt [13].

Fig. 1
figure 1

Hierarchical structure of JFMDA terminology 1st edition. Each terminology item has medical device problem and health problem

Terminology integration

In this study, the first edition of the JFMDA terminology was used. The 89 terminology sets recorded on separate pages of spreadsheets in Japanese were downloaded by the web page of JFMDA [14], and all terms were arranged in one CSV file (Fig. 2). The field names in the CSV file were as follows: “terminology ID,” “medical device problems/patient problems,” “category terms,” “preferred terms,” “synonyms,” “definitions,” and “CDRH-NCIt.” We defined the relationships between terms as follows: (1) a description of the hierarchy relations between category terms and preferred terms using “rdfs:subClassOf,” (2) a description of the relations between preferred terms and synonyms using “hasSynonym,” (3) a description of the relations between preferred terms and CDRH-NCIt terminology using “correspondenceOf.” (Fig. 3). We used Google Refine 2.5 [15] to describe and represent the relations using RDF (Fig. 4).

Fig. 2
figure 2

Conversion of spread sheet to CSV (English terms in parentheses)

Fig. 3
figure 3

Architecture of the relationships in JFMDA terminology

Fig. 4
figure 4

Conversion of CSV to RDF using Google Refine (Excerpt)

Structural inconsistency extraction using SPARQL

The terminology has a two-layer hierarchical structure of category terms and preferred terms. In addition, the definition statement attached to a preferred term has to be unique, the preferred terms should be unified in a terminology set, and the CDRH-NCIt terms must be uniquely determined. In order to evaluate inconsistencies in the integrated terminology, the following six matters were evaluated:

  1. (1)

    Is the two-layer structure between the category and preferred terms consistent without exchanging both terms depend on the terminology items?

  2. (2)

    Are preferred term and a synonym exchanged depend on the terminology items?

  3. (3)

    Is each preferred term subordinate to one category term?

  4. (4)

    Are the definitions of terms uniquely determined?

  5. (5)

    Are CDRH-NCIt terms corresponding to the preferred terms uniquely determined?

  6. (6)

    Is a term in a medical device problem used for a patient problem?

Detecting the six inconsistencies used SPARQL. In (1), in order to ensure consistency of the two-layer hierarchy in medical device and patient problems, it is not desirable that a term is present in both the category and the preferred terms. Otherwise, when the terminologies are integrated, three or more layers are created. Therefore, it is important to use a query to locate terms that belong to both category and preferred terms. The SPARQL query (Fig. 5) was adopted for the RDF of medical device problems and for that of the patient problems, respectively. The extracted terms that are present in both the category term and the preferred term were aggregated in a spreadsheet.

Fig. 5
figure 5

SPARQL query for inconsistency of the hierarchical structure. “Category_and_Preferred_term,” is the term that is present in both the category term and the preferred term. The number of “Category_and_Preferred_term” were aggregated

In (2), the modified query in Fig. 5 was applied to locate a term where a preferred term and a synonym are exchanged by different terminologies. The exchanged terms were aggregated in a spreadsheet. In (3), when a preferred term is subordinate to multiple category terms, it means the definition of the preferred term is not unique. We located the preferred terms which appear in several “category terms” using SPARQL and aggregated the number of the preferred terms. In (4), in order to detect inconsistencies in definition sentences, we extracted the combinations of the preferred terms these definition sentences and identified preferred terms with two or more definition sentences in a spreadsheet. In (5) in order to identify preferred terms with two or more CDRH-NCIt terms, we performed the same steps above. In (6), we detected terms in the sub classes of both “medical device problems” and “patient problems” in the two integrated terminologies.

We used the plugin “sparql-query-plugin-2.0.1” in the ontology editor Protégé 5.1.0 [16]. The correctness of the SPARQL query was verified by comparing with the original data.

Results

Summay of the number of terms relations

Before integrating JFMDA terminology, there were 1001 category terms for “medical device problems,” 730 for “patient problems,” 3382 preferred terms for “medical device problems,” and 3382 for “patient problems.” After the terminology integration using RDF, there were 1840 terms for “medical device problem,” and 1629 terms for “patient problem” together with category and preferred terms. The integration removed duplicated terms, a decrease of about 60% in the “medical device problem” and “patient problem.”

Before integrating JFMDA terminology, the numbers of relations in “medical device problems” were: “correspondenceOf” 2765, “hasSynonym” 2525, and “isDefinedBy” 3382. Those of patient problems were 2275, 1362, and 3164, respectively. After merging, duplicates were deleted and the following numbers of terms were obtained: in “medical device problems,” “correspondenceOf” 960, “hasSynonym” 456, and “isDefinedBy” 1250. In patient problems after merging, 667, 356, and 1202, respectively.

Inconsistency hierarchical structure in the category and preferred terms

As a result of the SPARQL search using query (1), 1457 patterns in medical device problem and 630 patterns in patient problem were found to be inconsistency hierarchies with exchanging category and preferred terms depending on the terminology items. Table 2 shows the examples of the inconsistency hierarchies. Except for duplicates, there were 69 terms (3.5%) in medical device problems which were among both “category” and “preferred terms,” and there were 79 terms (4.8%) in patient problems.

Table 2 Examples of unsatisfactory cases of the two-layer structure (English terms in parentheses) “Category and preferred term” is a term which change the allocation depending on the terminology item

Exchanging preferred term and synonym depend on the terminology item

Using query (2), there were 83 patterns of relationships among the “preferred terms,” “preferred terms and synonyms” among medical device problems and 54 patterns among patient problems (Table 3). Except for duplicates, there were 32 terms (1.7%) among medical device problems which were in both “category term” and “preferred term,” and there were 26 (1.6%) terms among patient problems.

Table 3 Examples of unsatisfactory cases of preferred terms and synonyms (English terms in parentheses)

Preferred terms which have several category terms

Using query (3), the number of preferred terms appearing in two or more category terms was 157 (9.5%) in medical device problem and 171 (10.5%) in patient problem. The maximum number of category terms that one preferred term has was 11 in medical device adverse event. In patient problem, the maximum number was 7. Examples are shown in Table 4.

Table 4 Examples of preferred terms which have several category terms (English terms in parentheses)

Definitions of terms that have multiple definitions

Using query (4), the preferred terms which have two or more definitions were 155 (8.4%) in medical device problem and there were 161 (9.9%) in patient problem. The adverse event term with the largest number of statements was “緩み(slack)” which had 10. In patient problem, “感染 (infection)” had 8 definitions and that was the largest number of all the preferred terms (Table 5).

Table 5 Examples of terms with multiple definitions (English terms in parentheses)

CDRH-NCIt terms corresponded to more than two preferred terms

Using query (5), the number of preferred terms which include two or more CDRH-NCIt terms were 160 (8.7%) in medical device problem and 95 (5.8%) in patient problem. The adverse event term with the largest number of CDRH-NCIt terms was 9 with “変形 (deformation).” In patient problem, “穿孔 (infection)” it was 4 CDRH-NCIt terms, and this was the largest number among the preferred terms (Table 6).

Table 6 Example of preferred terms which have more than two “CDRH-NCIt terms” (English terms in parentheses)

Terms among medical device problem included among patient problems

Using query (6), there were 8 terms which appeared in both medical device problem and patient problem. Table 7 shows the differences between the definition in medical device problem and in patient problem.

Table 7 Differences between the definition in medical device problem and in patient problem

Discussion

In this study, we integrated 89 items of terminology using RDF, and identified inconsistencies in the hierarchical structure, the relationship between synonyms and preferred terms, and the definition statements using SPARQL. About 60% of the total number of duplicated terms were found. This is because industry associations that created multiple terminologies adopted the same terms in terminologies of similar medical device groups. In the case that all terms with the same spelling have the same concept, efficient integration can be achieved automatically using RDF. Furthermore, we evaluated six matters of inconsistency in this study, terms that need to be reviewed terms accounted for about 10% or less than 10% in each item. It may take a lot of effort to detect these from thousands of words if it is done manually. Since SPARQL can do this automatically, it has the advantage of reducing the physical and time burden.

Inconsistency of two-layer hierarchies

In inconsistency hierarchical structure, 1457 patterns were found among medical device problems and 630 patterns among patient problems using SPARQL. Inconsistencies of two-level hierarchies can be approximately divided into four kinds: (1) category terms which indicate the schema and preferred terms which indicate the details such as the relationships between “faulty device” and “alarm abnormality” (Table 2), (2) the relationship between a category term and a preferred term as a cause-effect relationship, such as the relationships between “battery problem” and “early discharge” (Table 2) among medical device problems, and between “respiratory insufficiency” and “falling arterial oxygen saturation degree,” (3) the hierarchy levels of category terms and preferred terms are reversed (inverted) depending on the terminologies [For example, in one terminology “trauma” is listed as a category term (hypernym) and “injury” as a preferred term (hyponym), but in another terminology they are reversed], and (4) the same words are listed as category terms and preferred terms, such as for “erroneous puncturing” and “uncertain”.

In terminology development, the relationship between hypernym and hyponym is “is-a relation.” The relationship is reflexive and transitive, but not symmetric [17]. (1) is applicable to this rule. The reason why more than three levels are structured is that the granularity of the terms differs depending on industry associations. It is necessary to consider unifying the granularity among the industry associations. In (2), depending on the medical device manufacturer, it may be easier for users to describe the relationship between the category term and preferred term as a causal relationship rather than an inclusive relationship. However, when integrating multiple terminologies, if the inclusion and causal relationships are mixed, the preferred term belonging to the category term becomes inconsistent, which may cause difficulties for the user searching for a term. Accurate aggregation may also be hindered. It is necessary to request the industry association that created the term in which the relationships between the category terms and the preferred terms have a causal relationship to reconfirm the hierarchical structures they are basing it on, or to correct the term using a tool that classifies mechanically. In (3), the inversion of hypernyms and hyponyms means that they are homonymy. If it is correct, it is desirable to consider unifying the notation, and if not, consider unifying the order of hypernyms and hyponyms, or describing another notation. In (4), when developing the terminology using the bottom up method, it is considered that the category terms and the preferred terms became the same because there may have been no appropriate category terms. In addition, these may be autohyponyms [18]. Autohyponym indicates that the hyponym is a subset of the hypernym. In this case, unless exploring a different more appropriate notation or considering managing terms by ID number, there will be discrepancies in the hierarchical structure because the same notation is regarded as the same term by RDF.

Polyhierarchy

Some terminologies have adopted polyhierarchy. In SNOMED-CT, a subtype hierarchy is a directed acyclic graph [19]. Cimino described that general consensus seems to favor allowing multiple hierarchies to coexist in a vocabulary and one could be so designated with the others treated as nonhierarchical with directed and acyclic relationships [20]. It would be possible if JFMDA also became a valid directed acyclic graph when integrated consistently. However, some parts of this integrated terminology were cycle graph due to the inconsistency of the hierarchical structure and the cycle graph between two words as shown in (7).

One of the features of the preferred terms having a number of category terms among medical device problems expressed the cause of the category terms. For example, “battery defect” as the preferred term expressed the cause of “charging defect,” “malfunction,” and “defect” (Table 4). Among patient problems, the tendency of the relationship between category terms and the preferred terms is a pattern of cause-effect relation, such as “trauma” in the category term and “bone fracture” in the preferred term (Table 4). The relationship between category terms and the preferred terms is the opposite compared with that of medical device problems. Since there is a possibility that two terms that each industry association considers to be related are set as hypernyms and hyponyms, it will be necessary to request the industry association to make corrections. Although the JFMDA terminology has been developed based on a monohierarchy, it is necessary to consider allowing a multi-layered structure with directed acyclic graph while accepting differences in industry ideas.

Preferred term and synonym

There were 32 terms among medical device problems which appeared in both the category term and preferred term, and there were 26 terms in patient problem. The following three kinds were found: (1) according to the terminology, preferred terms and synonyms are opposite, such as “exfoliation” (剥離) and “peeling” (はがれ), (2) preferred terms and synonyms are connected by causal relationships, such as “deformation” (変形) and “cratering” (へこみ), and (3) preferred terms and synonyms seem to be connected by “is-a relation,” such as “aberrance” (迷入) and “subretinal migration” (網膜下迷入).

In (1), it should be unified to one or the other. In (2), “cratering”, “fold”, and “bend,” which are synonyms associated with “deformation,” and these terms mean the causes of the “deformation.” The term “deformation” includes various concepts from the views of different industry associations, therefore it may be interpreted differently depending on the industry association. It is preferred that spellings which describe the details are used as preferred terms and the others are adopted synonyms. “Deformation” should not be used as the preferred name, and “cratering”, “fold”, and “bend” are preferred as preferred terms instead. “Collapse,” “breakage,” and “curvature” should be used as synonyms. In (3), it is considered that the terms were used by omitting the part of spellings in some terminology fields. It is necessary to use the spellings expressed in detail as preferred terms in order to carry out accurate statistical analysis as above.

Definition statements

There were some patterns of definition statements. First of all, as patterns common to medical device problem and patient problem, whether there are punctuation marks or not. Secondly, various expressions were used to describe the same concept, such as “a state in which the ventricle of the heart trembles” and “convulsions” in “ventricular fibrillation.” Additionally, as to the specific pattern in medical device problem, some definitions included the cause of the adverse event and the others did not include it, such as “blown fuse.” As to the specific pattern in patient problem, one is whether the cause of the patient problem is included or not as well as the medical adverse event, another is the cause of the patient problem is different, such as “extended operation time,” the other is whether to include a countermeasure for patient problem or not, such as “iridocele.” Those who use the JFMDA terminology may be confused if multiple definitions are given to one term. Therefore, if plural definition statements express the same concept, they should unify the description. If not, constructors should use different terms.

Multilingual mapping

There are international terminologies for medical devices related to adverse events, especially IMDRF, a voluntary group of medical device regulators from around the world that has constructed the terminology to accelerate international medical device regulatory harmonization and convergence [2]. Multilingual terminology mapping is an important process of finding correspondences between terminologies in different languages to allow these to be mutually understandable.

In JFMDA terminology 1st edition, mapping between the preferred terms and CDRH-NCIt terms was conducted manually by the industry associations. The result of an inconsistency detection survey using SPARQL showed that there were 160 preferred terms in JFMDA which appear in two or more CDRH-NCIt terms in medical device problems, and there were 95 among patient problems. This may be due to differences in the interpretation of CDRH-NCIt terms among industry associations. However it is also possible that the Japanese concept and the English concept do not exactly match. In ontology, there are two main strategies for alignment: direct and indirect alignment. The direct alignment is translation-based and uses external resources to help with the translation, while the indirect alignment uses intermediary mapping between the source and target ontologies. In addition, mapping two ontologies can be an automated or manual process [21]. Manual mapping is still the most common choice, but it necessitates a large team of experts; it is time consuming and prone to errors. Meanwhile, automated methods use publicly available terminology resources, but the sources of these are largely incomplete outside of the English speaking world [21]. To improve inconsistencies, it is being considered to reduce the human and time resources required and ensure accuracy using the following process: translating the international (not available in Japanese) terminology into Japanese, performing automatic mapping using machine learning, and confirming the results manually.

Whether a term among medical device problems is included in patient problems

There were 8 terms which are present among both medical device problems and patient problems. These terms share the concept, but depending on the situation where they are used, the subject can be the device or the patient. Therefore, this term was included among both medical device problems and patient problems. However, if the same notation is used, there is a problem that the hierarchies of terms in medical device problem, and those in patient problem are exchanged in mapping by RDF. Therefore, it is necessary to change the notation such as “damage (medical device problem)” and “damage (patient problem)” to distinguish both.

Limitations and future work

A previous study [4] identified six problems in terminologies: incorrect schemas, misunderstanding of semantics of attributes, incomplete modelling, over-literal definitions, not tracing errors to their roots, and lack of normalisation. “Incorrect schemas,” “misunderstanding of semantics of attributes” and “not traced, in detecting structural problems” correspond to polyhierarchy in this study. “Incomplete modeling” corresponds to the determination of preferred terms with multiple definition statements. For the other problems, it is necessary to perform an analysis considering the concepts of the terms and definition statements. However, it is difficult to do this using RDF and SPARQL. In synonym determination, we are conducting research using edit distance and distributed representation using definition statements of preferred terms [22]. In addition, a rule-based method for detecting synonyms from word notation has also been proposed [23]. We believe that applying these technologies will work effectively to improve the inconsistencies in terminology mapping.

Conclusions

In this study, we integrated JFMDA terminology and identified 6 items of inconsistencies. The RDF and SPARQL are useful tools to explore inconsistencies of hierarchies, definition statements, and synonyms when integrating terminology by term notation automatically. As future work, we will consider a method which can take into account concepts of terms in order to improve the inconsistency detection method.

Availability of data and materials

The data that support the findings of this study are available from The Japan Federation of Medical Devices Associations (jfmda.gr.jp/activity/committee/fuguai/).

Abbreviations

CDRH-NCIt:

The centers for devices and radiological health and the national cancer institute’s thesaurus

IMDRF:

The International Medical Device Regulators Forum

ISO TC:

The International Organization for Standardization Technical Committees

JFMDA:

The Japan Federation of Medical Devices Associations

RDF:

Resource Description Framework

SPARQL:

SPARQL Protocol and RDF Query Language

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Acknowledgements

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Funding

This research is supported by the Research on Regulatory Science of Pharmaceuticals and Medical Devices from the Japan Agency for Medical Research and development, AMED (Grant Number 21mk0102150h0003). The funder had no role in the design of the study, analysis, and interpretation of the data, or the writing of the manuscript.

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AY: Conceptualization, Methodology, Software, Data curation, Investigation, Writing- Original draft preparation. HY: Supervision, Writing- Reviewing and Editing. All authors read and approved the final manuscript.

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Correspondence to Ayako Yagahara.

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Yagahara, A., Yokoi, H. Terminology integration and inconsistency identification of adverse event terminology for Japanese medical devices using SPARQL. BMC Med Inform Decis Mak 22, 16 (2022). https://doi.org/10.1186/s12911-022-01748-2

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Keywords

  • Terminology mapping
  • Inconsistency detection
  • Medical device
  • Adverse event
  • Resource Description Framework
  • SPARQL Protocol and RDF Query Language