Utility of SNOMED CT in automated expansion of clinical terms in discharge summaries: Testing issues of coverage

Alani, H, Kim, Sanghee, Millard, DE, et al. (2003) Automatic ontology-based knowledge extraction from web documents. IEEE Intelligent Systems 18(1): 14–21.
Google Scholar | Crossref Bietenbeck, A, Boeker, M, Schulz, S (2018) NPU, LOINC, and SNOMED CT: a comparison of terminologies for laboratory results reveals individual advantages and a lack of possibilities to encode interpretive comments. LaboratoriumsMedizin 42(6): 267–275.
Google Scholar | Crossref Bodenreider, J (2018) The New SNOMED CT International Medicinal Product Model. In: Corvallis, Oregon, USA, 2018. International Conference on Biological Ontology 2018. Available at: http://ceur-ws.org/Vol-2285/ICBO_2018_paper_36.pdf. (accessed 21 January 2016)
Google Scholar Bona, JP, Ceusters, W (2018) Mismatches between major subhierarchies and semantic tags in SNOMED CT. Journal of Biomedical Informatics 81: 1–15.
Google Scholar | Crossref | Medline Elkin, PL, Brown, SH, Husser, CS, et al. (2006) Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists. Mayo Clinic Proceedings 81(6): 741–748.
Google Scholar | Crossref | Medline | ISI International Health Terminology Standards Development Organization (IHTSDO) (2020a) 4. SNOMED CT Basics – SNOMED CT Starter Guide - SNOMED Confluence. Available at: https://confluence.ihtsdotools.org/display/DOCSTART/4.+SNOMED+CT+Basics (accessed 10 May 2020).
Google Scholar International Health Terminology Standards Development Organization (IHTSDO) (2020b) SOMED international extensions practical guide. Available at: https://confluence.ihtsdotools.org/display/DOCEXTPG/Extensions+Practical+Guide (accessed 22 April 2020).
Google Scholar Khorrami, F, Ahmadi, M, Sheikhtaheri, A (2018) Evaluation of SNOMED CT content coverage: a systematic literature review. Studies in health technology and informatics 248: 212.
Google Scholar | Medline Lee, D, Cornet, R, Lau, F, et al. (2013) A survey of SNOMED CT implementations. Journal of Biomedical Informatics 46(1): 87–96.
Google Scholar | Crossref | Medline Li, N, Motta, E (2010) Evaluations of user-driven ontology summarization. In: Cimiano, P, Pinto, HS (eds) Knowledge Engineering and Management by the Masses. Berlin, Heidelberg: Lecture Notes in Computer Science. Springer, pp. 544–553.
Google Scholar | Crossref Liu, H, Hildebrand, PL, Perl, Y, et al. (2018) Enrichment of SNOMED CT ophthalmology component to support EHR coding. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), , pp. 1990–1997. DOI: 10.1109/BIBM.2018.8621272.
Google Scholar | Crossref Maiga, G, Ddembe, W (2009) A flexible biomedical ontology selection tool. Available at: http://makir.mak.ac.ug/handle/10570/2021 (accessed 7 July 2019).
Google Scholar Martínez-Romero, M, Jonquet, C, O’Connor, MJ, et al. (2017) NCBO ontology recommender 2.0: an enhanced approach for biomedical ontology recommendation. Journal of Biomedical Semantics 8(1): 21.
Google Scholar | Crossref | Medline Martínez-Romero, M, Vázquez-Naya, JM, Pereira, J, et al. (2014) BiOSS: a system for biomedical ontology selection. Computer Methods and Programs in Biomedicine 114(1): 125–140.
Google Scholar | Crossref | Medline Melton, GB, Morrison, FP, Cimino, JJ, et al. (2006) How well do electronic systems represent colorectal cancer surgery concepts? Evaluation of SNOMED-CT, ICD9-CM, and CPT-4 for content coverage. Journal of the American College of Surgeons 203(3, Supplement). Abstracts for the 61st Annual Sessions of the Forum on Fundamental Surgical Problems S69–S70. DOI: 10.1016/j.jamcollsurg.2006.05.182.
Google Scholar | Crossref Miñarro-Giménez, JA, Martínez-Costa, C, Karlsson, D, et al. (2018) Qualitative analysis of manual annotations of clinical text with SNOMED CT. PLOS ONE 13(12): e0209547.
Google Scholar | Crossref | Medline Mujib, MI, Yang, CC, Zhao, M, et al. (2018) Expanding consumer health vocabularies with frequency-conserving internal context models. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), , pp. 241–246. DOI: 10.1109/ICHI.2018.00034.
Google Scholar | Crossref NLM.govt (2019a) UMLS Metathesaurus – LNC (LOINC) – Statistics. Available at: https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/LNC/stats.html (accessed 30 June 2019).
Google Scholar NLM.govt (2019b) UMLS Metathesaurus – NCI (NCI Thesaurus) – Statistics. Available at: https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NCI/stats.html (accessed 30 June 2019).
Google Scholar Porter, MF (1980) An algorithm for suffix stripping. Program 14(3): 130–137.
Google Scholar | Crossref Raje, S, Bodenreider, O (2017) Interoperability of disease concepts in clinical and research ontologies: contrasting coverage and structure in the disease ontology and SNOMED CT. Studies in health technology and informatics 245: 925–929.
Google Scholar | Medline Rastegar-Mojarad, M, Sohn, S, Wang, L, et al. (2017) Need of informatics in designing interoperable clinical registries. International journal of medical informatics 108: 78–84.
Google Scholar | Crossref | Medline Rodrigues, J, Schulz, S, Mizen, B, et al. (2018) Is the application of SNOMED CT concept model sufficiently quality assured? AMIA Annual Symposium Proceedings 2017: 1488–1497.
Google Scholar | Medline Schriml, LM, Arze, C, Nadendla, S, et al. (2012) Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Research 40(D1): D940–D946.
Google Scholar | Crossref | Medline SNOMED International (2019) Compositional Grammar – Specification and Guide. Available at: https://confluence.ihtsdotools.org/display/DOCSCG (accessed 28 April 2019).
Google Scholar Stokes, N, Li, Y, Cavedon, L, et al. (2009) Exploring criteria for successful query expansion in the genomic domain. Information Retrieval 12(1): 17–50.
Google Scholar | Crossref Uzuner, Ö, Luo, Y, Szolovits, P (2007) Evaluating the state-of-the-art in automatic de-identification. Journal of the American Medical Informatics Association 14(5): 550–563.
Google Scholar | Crossref | Medline | ISI Wang, Y, Patrick, J, Miller, G, et al. (2008) A computational linguistics motivated mapping of ICPC-2 PLUS to SNOMED CT. BMC Medical Informatics and Decision Making 8(1): S5.
Google Scholar | Crossref | Medline Xu, J, Croft, WB (1998) Corpus-based stemming using cooccurrence of word variants. ACM Trans. Inf. Syst 16(1): 61–81.
Google Scholar | Crossref | ISI Zivaljevic, A, Atalag, K, Warren, J, et al. (2015) Annotation of clinical datasets using openEHR Archetypes as a solution for data access issues faced in biomedical projects. Health Informatics New Zealand 2015. Available at: https://www.researchgate.net/profile/Aleksandar_Zivaljevic/publication/282278618_Annotation_of_clinical_datasets_using_openEHR_Archetypes_as_a_solution_for_data_access_issues_faced_in_biomedical_projects/links/560a475b08ae576ce63fbbfd.pdf (accessed 21 January 2016).
Google Scholar

留言 (0)

沒有登入
gif