Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients
Abstract Background Two years into the COVID-19 pandemic and with more than five million deaths worldwide, the healthcare establishment continues to struggle with every new wave of the pandemic resulting from a new coronavirus variant. Research has demonstrated that there are variations in the sympt...
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| Format: | Article |
| Language: | English |
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BMC
2023-02-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02136-0 |
| _version_ | 1827114519186374656 |
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| author | Vipina K. Keloth Shuxin Zhou Luke Lindemann Ling Zheng Gai Elhanan Andrew J. Einstein James Geller Yehoshua Perl |
| author_facet | Vipina K. Keloth Shuxin Zhou Luke Lindemann Ling Zheng Gai Elhanan Andrew J. Einstein James Geller Yehoshua Perl |
| author_sort | Vipina K. Keloth |
| collection | DOAJ |
| description | Abstract Background Two years into the COVID-19 pandemic and with more than five million deaths worldwide, the healthcare establishment continues to struggle with every new wave of the pandemic resulting from a new coronavirus variant. Research has demonstrated that there are variations in the symptoms, and even in the order of symptom presentations, in COVID-19 patients infected by different SARS-CoV-2 variants (e.g., Alpha and Omicron). Textual data in the form of admission notes and physician notes in the Electronic Health Records (EHRs) is rich in information regarding the symptoms and their orders of presentation. Unstructured EHR data is often underutilized in research due to the lack of annotations that enable automatic extraction of useful information from the available extensive volumes of textual data. Methods We present the design of a COVID Interface Terminology (CIT), not just a generic COVID-19 terminology, but one serving a specific purpose of enabling automatic annotation of EHRs of COVID-19 patients. CIT was constructed by integrating existing COVID-related ontologies and mining additional fine granularity concepts from clinical notes. The iterative mining approach utilized the techniques of 'anchoring' and 'concatenation' to identify potential fine granularity concepts to be added to the CIT. We also tested the generalizability of our approach on a hold-out dataset and compared the annotation coverage to the coverage obtained for the dataset used to build the CIT. Results Our experiments demonstrate that this approach results in higher annotation coverage compared to existing ontologies such as SNOMED CT and Coronavirus Infectious Disease Ontology (CIDO). The final version of CIT achieved about 20% more coverage than SNOMED CT and 50% more coverage than CIDO. In the future, the concepts mined and added into CIT could be used as training data for machine learning models for mining even more concepts into CIT and further increasing the annotation coverage. Conclusion In this paper, we demonstrated the construction of a COVID interface terminology that can be utilized for automatically annotating EHRs of COVID-19 patients. The techniques presented can identify frequently documented fine granularity concepts that are missing in other ontologies thereby increasing the annotation coverage. |
| first_indexed | 2024-04-09T22:52:47Z |
| format | Article |
| id | doaj.art-55fcbb97e35f4ff28f9c7fa353236747 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2025-03-20T12:03:10Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-55fcbb97e35f4ff28f9c7fa3532367472024-09-15T11:22:24ZengBMCBMC Medical Informatics and Decision Making1472-69472023-02-0123S111810.1186/s12911-023-02136-0Mining of EHR for interface terminology concepts for annotating EHRs of COVID patientsVipina K. Keloth0Shuxin Zhou1Luke Lindemann2Ling Zheng3Gai Elhanan4Andrew J. Einstein5James Geller6Yehoshua Perl7School of Biomedical Informatics, University of Texas Health Science Center at HoustonDepartment of Computer Science, New Jersey Institute of TechnologySchool of Medicine and Health Sciences, The George Washington UniversityComputer Science and Software Engineering Department, Monmouth UniversityRenown Institute for Health Innovation, Desert Research InstituteCardiology Division, Department of Medicine, Columbia University Irving Medical CenterDepartment of Computer Science, New Jersey Institute of TechnologyDepartment of Computer Science, New Jersey Institute of TechnologyAbstract Background Two years into the COVID-19 pandemic and with more than five million deaths worldwide, the healthcare establishment continues to struggle with every new wave of the pandemic resulting from a new coronavirus variant. Research has demonstrated that there are variations in the symptoms, and even in the order of symptom presentations, in COVID-19 patients infected by different SARS-CoV-2 variants (e.g., Alpha and Omicron). Textual data in the form of admission notes and physician notes in the Electronic Health Records (EHRs) is rich in information regarding the symptoms and their orders of presentation. Unstructured EHR data is often underutilized in research due to the lack of annotations that enable automatic extraction of useful information from the available extensive volumes of textual data. Methods We present the design of a COVID Interface Terminology (CIT), not just a generic COVID-19 terminology, but one serving a specific purpose of enabling automatic annotation of EHRs of COVID-19 patients. CIT was constructed by integrating existing COVID-related ontologies and mining additional fine granularity concepts from clinical notes. The iterative mining approach utilized the techniques of 'anchoring' and 'concatenation' to identify potential fine granularity concepts to be added to the CIT. We also tested the generalizability of our approach on a hold-out dataset and compared the annotation coverage to the coverage obtained for the dataset used to build the CIT. Results Our experiments demonstrate that this approach results in higher annotation coverage compared to existing ontologies such as SNOMED CT and Coronavirus Infectious Disease Ontology (CIDO). The final version of CIT achieved about 20% more coverage than SNOMED CT and 50% more coverage than CIDO. In the future, the concepts mined and added into CIT could be used as training data for machine learning models for mining even more concepts into CIT and further increasing the annotation coverage. Conclusion In this paper, we demonstrated the construction of a COVID interface terminology that can be utilized for automatically annotating EHRs of COVID-19 patients. The techniques presented can identify frequently documented fine granularity concepts that are missing in other ontologies thereby increasing the annotation coverage.https://doi.org/10.1186/s12911-023-02136-0Interface terminologyCOVID-19 ontologiesConcept miningEHR annotation |
| spellingShingle | Vipina K. Keloth Shuxin Zhou Luke Lindemann Ling Zheng Gai Elhanan Andrew J. Einstein James Geller Yehoshua Perl Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients BMC Medical Informatics and Decision Making Interface terminology COVID-19 ontologies Concept mining EHR annotation |
| title | Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients |
| title_full | Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients |
| title_fullStr | Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients |
| title_full_unstemmed | Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients |
| title_short | Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients |
| title_sort | mining of ehr for interface terminology concepts for annotating ehrs of covid patients |
| topic | Interface terminology COVID-19 ontologies Concept mining EHR annotation |
| url | https://doi.org/10.1186/s12911-023-02136-0 |
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