Benedikt Fh Becker1, Jan A Kors2, Erik M van Mulligen2, Miriam Cjm Sturkenboom2. 1. Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, Rotterdam, 3015, GE, The Netherlands. benedikt.becker@posteo.de. 2. Department of Medical Informatics, Erasmus University Medical Center, Dr. Molewaterplein 50, Rotterdam, 3015, GE, The Netherlands.
Abstract
BACKGROUND: Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines. METHODS: We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions. We implemented five algorithms for the alignment of vaccine coding systems, i.e., the identification of corresponding codes from different coding ystems, based on an analysis of the code descriptors. The algorithms were evaluated by comparing their results with manually created alignments in two reference sets including clinical and database-specific coding systems with multilingual code descriptors. RESULTS: The best-performing algorithm represented code descriptors as logical statements about entities in the VaccO ontology and used an ontology reasoner to infer common properties and identify corresponding vaccine codes. The evaluation demonstrated excellent performance of the approach (F-scores 0.91 and 0.96). CONCLUSION: The VaccO ontology allows the identification, representation, and comparison of heterogeneous descriptions of vaccines. The automatic alignment of vaccine coding systems can accelerate the readiness of EHR databases in collaborative vaccine studies.
BACKGROUND: Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines. METHODS: We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions. We implemented five algorithms for the alignment of vaccine coding systems, i.e., the identification of corresponding codes from different coding ystems, based on an analysis of the code descriptors. The algorithms were evaluated by comparing their results with manually created alignments in two reference sets including clinical and database-specific coding systems with multilingual code descriptors. RESULTS: The best-performing algorithm represented code descriptors as logical statements about entities in the VaccO ontology and used an ontology reasoner to infer common properties and identify corresponding vaccine codes. The evaluation demonstrated excellent performance of the approach (F-scores 0.91 and 0.96). CONCLUSION: The VaccO ontology allows the identification, representation, and comparison of heterogeneous descriptions of vaccines. The automatic alignment of vaccine coding systems can accelerate the readiness of EHR databases in collaborative vaccine studies.
Authors: Miriam Sturkenboom; Toon Braeye; Lieke van der Aa; Giorgia Danieli; Caitlin Dodd; Talita Duarte-Salles; Hanne-Dorthe Emborg; Marius Gheorghe; Johnny Kahlert; Rosa Gini; Consuelo Huerta-Alvarez; Elisa Martín-Merino; Chris McGee; Simon de Lusignan; Gino Picelli; Giuseppe Roberto; Lara Tramontan; Marco Villa; Daniel Weibel; Lina Titievsky Journal: Vaccine Date: 2020-02-12 Impact factor: 3.641