Extending import detection algorithms for concept import from two to three biomedical terminologies
Abstract Background While 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-020-01290-z |
| _version_ | 1818601275149778944 |
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| author | Vipina K. Keloth James Geller Yan Chen Julia Xu |
| author_facet | Vipina K. Keloth James Geller Yan Chen Julia Xu |
| author_sort | Vipina K. Keloth |
| collection | DOAJ |
| description | Abstract Background While 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. Methods In 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. Results The 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. Conclusion We showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology. |
| first_indexed | 2024-12-16T12:48:47Z |
| format | Article |
| id | doaj.art-b93daefee97f4a3a8d1041e4fe7ccb1c |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-16T12:48:47Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-b93daefee97f4a3a8d1041e4fe7ccb1c2022-12-21T22:31:13ZengBMCBMC Medical Informatics and Decision Making1472-69472020-12-0120S1011110.1186/s12911-020-01290-zExtending import detection algorithms for concept import from two to three biomedical terminologiesVipina K. Keloth0James Geller1Yan Chen2Julia Xu3Department of Computer Science, New Jersey Institute of TechnologyDepartment of Computer Science, New Jersey Institute of TechnologyDepartment of Computer Information Systems, Borough of Manhattan Community College, City University of New YorkJXU ConsultingAbstract Background While 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. Methods In 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. Results The 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. Conclusion We showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology.https://doi.org/10.1186/s12911-020-01290-zTerminologiesUMLSConcept importSNOMED CTNational cancer institute thesaurusDensity differences |
| spellingShingle | Vipina K. Keloth James Geller Yan Chen Julia Xu Extending import detection algorithms for concept import from two to three biomedical terminologies BMC Medical Informatics and Decision Making Terminologies UMLS Concept import SNOMED CT National cancer institute thesaurus Density differences |
| title | Extending import detection algorithms for concept import from two to three biomedical terminologies |
| title_full | Extending import detection algorithms for concept import from two to three biomedical terminologies |
| title_fullStr | Extending import detection algorithms for concept import from two to three biomedical terminologies |
| title_full_unstemmed | Extending import detection algorithms for concept import from two to three biomedical terminologies |
| title_short | Extending import detection algorithms for concept import from two to three biomedical terminologies |
| title_sort | extending import detection algorithms for concept import from two to three biomedical terminologies |
| topic | Terminologies UMLS Concept import SNOMED CT National cancer institute thesaurus Density differences |
| url | https://doi.org/10.1186/s12911-020-01290-z |
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