Semantic enrichment of Pomeranian health study data using LOINC and WHO-FIC terminology mapping principles

Objective To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. Materials and Methods We employed a workflow that maps codes from the SH...

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Vydáno v:JAMIA open Ročník 8; číslo 2; s. ooaf010
Hlavní autoři: Inau, Esther Thea, Radke, Dörte, Bird, Linda, Westphal, Susanne, Ittermann, Till, Schäfer, Christian, Nauck, Matthias, Zeleke, Atinkut Alamirrew, Schmidt, Carsten Oliver, Waltemath, Dagmar
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Oxford University Press 01.04.2025
Témata:
Computational linguistics
Data dictionaries
Laboratories
Language processing
Natural language interfaces
Research and Applications
Semantics
Terms and phrases
South Korea
Germany
LOINC
WHO-FIC
FAIR
Study of Health in Pomerania
data dictionary
semantic enrichment
ISSN:2574-2531, 2574-2531
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Abstract Objective To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. Materials and Methods We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network. Results We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches. Discussion We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well. Conclusion Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies. Lay Summary The Logical Observation Identifiers Names and Codes (LOINC) is an internationally recognized coding system that supports the unique identification and coding of medical laboratory tests. This study aimed to semantically enrich the SHIP-START-4 laboratory data dictionary by linking it to LOINC thus making it more findable, accessible, interoperable, and reusable (FAIR). The mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network were implemented in the semantic enrichment. Seventy-one out of 72 codes from the SHIP-START-4 laboratory data dictionary were successfully mapped to LOINC codes. This semantic enrichment enhances data retrieval, integration, and reuse. It is important to remember that some clinically relevant data is not covered by LOINC and must be considered separately. This work illustrates the importance of early collaboration in data annotation for better interoperability in future cohort studies. It also illustrates that it is better to perform annotations on data dictionaries before data collection starts instead of afterwards. The experiences gained while conducting this work may be relevant for other researchers who seek to develop a standard method for annotating medical data dictionaries for population studies.
AbstractList Objective: To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. Materials and Methods: We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network. Results: We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches. Discussion: We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well. Conclusion: Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies. Lay Summary The Logical Observation Identifiers Names and Codes (LOINC) is an internationally recognized coding system that supports the unique identification and coding of medical laboratory tests. This study aimed to semantically enrich the SHIP-START-4 laboratory data dictionary by linking it to LOINC thus making it more findable, accessible, interoperable, and reusable (FAIR). The mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network were implemented in the semantic enrichment. Seventy-one out of 72 codes from the SHIP-START-4 laboratory data dictionary were successfully mapped to LOINC codes. This semantic enrichment enhances data retrieval, integration, and reuse. It is important to remember that some clinically relevant data is not covered by LOINC and must be considered separately. This work illustrates the importance of early collaboration in data annotation for better interoperability in future cohort studies. It also illustrates that it is better to perform annotations on data dictionaries before data collection starts instead of afterwards. The experiences gained while conducting this work may be relevant for other researchers who seek to develop a standard method for annotating medical data dictionaries for population studies. Key words: semantic enrichment; LOINC; data dictionary; Study of Health in Pomerania; FAIR; WHO-FIC.
To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.ObjectiveTo semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network.Materials and MethodsWe employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network.We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches.ResultsWe were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches.We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well.DiscussionWe increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well.Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies.ConclusionSemantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies.
To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network. We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches. We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well. Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies.
Lay Summary
Objective To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. Materials and Methods We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network. Results We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches. Discussion We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well. Conclusion Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies.
Objective To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. Materials and Methods We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network. Results We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches. Discussion We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well. Conclusion Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies. Lay Summary The Logical Observation Identifiers Names and Codes (LOINC) is an internationally recognized coding system that supports the unique identification and coding of medical laboratory tests. This study aimed to semantically enrich the SHIP-START-4 laboratory data dictionary by linking it to LOINC thus making it more findable, accessible, interoperable, and reusable (FAIR). The mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network were implemented in the semantic enrichment. Seventy-one out of 72 codes from the SHIP-START-4 laboratory data dictionary were successfully mapped to LOINC codes. This semantic enrichment enhances data retrieval, integration, and reuse. It is important to remember that some clinically relevant data is not covered by LOINC and must be considered separately. This work illustrates the importance of early collaboration in data annotation for better interoperability in future cohort studies. It also illustrates that it is better to perform annotations on data dictionaries before data collection starts instead of afterwards. The experiences gained while conducting this work may be relevant for other researchers who seek to develop a standard method for annotating medical data dictionaries for population studies.
Audience Academic
Author Westphal, Susanne
Zeleke, Atinkut Alamirrew
Radke, Dörte
Bird, Linda
Schmidt, Carsten Oliver
Inau, Esther Thea
Ittermann, Till
Nauck, Matthias
Schäfer, Christian
Waltemath, Dagmar
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Issue 2
Keywords LOINC
WHO-FIC
FAIR
Study of Health in Pomerania
data dictionary
semantic enrichment
Language English
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Snippet Objective To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to...
To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better...
Objective: To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to...
Lay Summary
Objective To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to...
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StartPage ooaf010
SubjectTerms Computational linguistics
Data dictionaries
Laboratories
Language processing
Natural language interfaces
Research and Applications
Semantics
Terms and phrases
Title Semantic enrichment of Pomeranian health study data using LOINC and WHO-FIC terminology mapping principles
URI https://www.ncbi.nlm.nih.gov/pubmed/40059976
https://www.proquest.com/docview/3201502325
https://www.proquest.com/docview/3175683389
https://pubmed.ncbi.nlm.nih.gov/PMC11884810
Volume 8
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