This paper is in the following e-collection/theme issue:

Ontologies, Classifications, and Coding (227) Electronic Health Records (971)

Published on in Vol 7, No 1 (2019): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11487, first published .
SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

Authors of this article:

Ling Chu1 Author Orcid Image ;   Vaishnavi Kannan1 Author Orcid Image ;   Mujeeb A Basit1 Author Orcid Image ;   Diane J Schaeflein1 Author Orcid Image ;   Adolfo R Ortuzar1 Author Orcid Image ;   Jimmie F Glorioso1 Author Orcid Image ;   Joel R Buchanan2 Author Orcid Image ;   Duwayne L Willett1 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

  1. Hajizadeh A, Lotfi T, Falzon D, Mertz D, Nieuwlaat R, Gebreselassie N, Jaramillo E, Korobitsyn A, Zignol M, Mirzayev F, Ismail N, Brozek J, Loeb M, Piggott T, Darzi A, Wang Q, Mahmood A, Saroey P, Matthews M, Schünemann F, Dietl B, Nowak A, Kulesza K, Muti-Schünemann G, Bognanni A, Charide R, Akl E, Kasaeva T, Schünemann H. Recommendation mapping of the World Health Organization's guidelines on tuberculosis: A new approach to digitizing and presenting recommendations. Journal of Clinical Epidemiology 2021;134:138 View
  2. Chandra A, Philips S, Pandey A, Basit M, Kannan V, Sara E, Das S, Lee S, Haley B, Willett D, Zaha V. Electronic Health Records–Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study. JMIR Cardio 2021;5(1):e22296 View
  3. Chang E, Mostafa J. The use of SNOMED CT, 2013-2020: a literature review. Journal of the American Medical Informatics Association 2021;28(9):2017 View
  4. Vuokko R, Vakkuri A, Palojoki S. Systematized Nomenclature of Medicine–Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review. JMIR Medical Informatics 2023;11:e43750 View
  5. Elkheder M, Gonzalez-Izquierdo A, Qummer Ul Arfeen M, Kuan V, Lumbers R, Denaxas S, Shah A. Translating and evaluating historic phenotyping algorithms using SNOMED CT. Journal of the American Medical Informatics Association 2023;30(2):222 View
  6. Rinaldi E, Drenkhahn C, Gebel B, Saleh K, Tönnies H, von Loewenich F, Thoma N, Baier C, Boeker M, Hinske L, Diaz L, Behnke M, Ingenerf J, Thun S. Towards interoperability in infection control: a standard data model for microbiology. Scientific Data 2023;10(1) View
  7. Matthewman J, Andresen K, Suffel A, Lin L, Schultze A, Tazare J, Bhaskaran K, Williamson E, Costello R, Quint J, Strongman H. Checklist and guidance on creating codelists for electronic health records research. NIHR Open Research 2024;4:20 View
  8. Senior R, Tsai T, Ratliff W, Nadler L, Balu S, Malcolm E, McPeek Hinz E. Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study. JMIR Medical Informatics 2024;12:e51274 View