Published on in Vol 2, No 1 (2014): Jan-Jun

Next Generation Phenotyping Using the Unified Medical Language System

Next Generation Phenotyping Using the Unified Medical Language System

Next Generation Phenotyping Using the Unified Medical Language System

Journals

  1. Mbagwu M, French D, Gill M, Mitchell C, Jackson K, Kho A, Bryar P. Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes. JMIR Medical Informatics 2016;4(2):e14 View
  2. Voigt A, Saba S. The Truth Is in the Details. Circulation 2018;137(1):34 View
  3. Beeksma M, Verberne S, van den Bosch A, Das E, Hendrickx I, Groenewoud S. Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records. BMC Medical Informatics and Decision Making 2019;19(1) View
  4. Garcelon N, Burgun A, Salomon R, Neuraz A. Electronic health records for the diagnosis of rare diseases. Kidney International 2020;97(4):676 View
  5. Lin L, Liang W, Li C, Huang X, Lv J, Peng H, Wang B, Zhu B, Sun Y. Development and implementation of a dynamically updated big data intelligence platform from electronic health records for nasopharyngeal carcinoma research. The British Journal of Radiology 2019;92(1102) View
  6. Reimer A, Milinovich A. Using UMLS for electronic health data standardization and database design. Journal of the American Medical Informatics Association 2020;27(10):1520 View
  7. Sendak M, Balu S, Schulman K. Barriers to Achieving Economies of Scale in Analysis of EHR Data. Applied Clinical Informatics 2017;08(03):826 View
  8. Zheng F, Shi J, Yang Y, Zheng W, Cui L. A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System. Journal of the American Medical Informatics Association 2020;27(10):1568 View
  9. Chu L, Kannan V, Basit M, Schaeflein D, Ortuzar A, Glorioso J, Buchanan J, Willett D. SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets. JMIR Medical Informatics 2019;7(1):e11487 View
  10. Chen D, Zhang R, Feng J, Liu K. Fulfilling information needs of patients in online health communities. Health Information & Libraries Journal 2020;37(1):48 View
  11. Nguyen T, Zhang T, Fox G, Zeng S, Cao N, Pan C, Chen J. Linking clinotypes to phenotypes and genotypes from laboratory test results in comprehensive physical exams. BMC Medical Informatics and Decision Making 2021;21(S3) View
  12. 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
  13. Jing X. UMLS at 30 years: How it is used and published (Preprint). JMIR Medical Informatics 2020 View
  14. Zheng F, Abeysinghe R, Cui L. Identification of missing concepts in biomedical terminologies using sequence-based formal concept analysis. BMC Medical Informatics and Decision Making 2021;21(S7) View
  15. Chen Z, Wang X, Xiao L, Sun J, Mao M, Zhang H, Guan J. Construction and application of nasopharyngeal carcinoma-specific big data platform based on electronic health records. American Journal of Otolaryngology 2024;45(3):104204 View