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

Reviews in Medical Informatics (92) Clinical Information and Decision Making (1398) Cardiology (157) Electronic Health Records (1050)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23934, first published .
Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

Authors of this article:

Seungwon Lee1, 2, 3, 4 Author Orcid Image ;   Chelsea Doktorchik1, 2 Author Orcid Image ;   Elliot Asher Martin1, 3 Author Orcid Image ;   Adam Giles D'Souza1, 3 Author Orcid Image ;   Cathy Eastwood1, 2 Author Orcid Image ;   Abdel Aziz Shaheen1, 2, 5 Author Orcid Image ;   Christopher Naugler2, 6 Author Orcid Image ;   Joon Lee1, 2, 4, 7 Author Orcid Image ;   Hude Quan1, 2 Author Orcid Image
Article Authors Cited by (15) Tweetations (2) Metrics

Journals

  1. Kosowan L, Singer A, Zulkernine F, Zafari H, Nesca M, Muthumuni D. Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study. JMIR Medical Informatics 2022;10(12):e41312 View
  2. Das A, Joseph J. The microbiological landscape and epidemiology of ocular infections in a multi-tier ophthalmology network in India: an electronic medical record driven analytics report. Eye 2023;37(8):1590 View
  3. Martin E, D’Souza A, Lee S, Doktorchik C, Eastwood C, Quan H. Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study. CMAJ Open 2023;11(1):E131 View
  4. Boussat B, Cazzorla F, Le Marechal M, Pavese P, Mounayar A, Sellier E, Gaillat J, Camara B, Degano B, Maillet M, Courtois X, Bouisse M, Seigneurin A, François P. Incidence of Avoidable 30-Day Readmissions Following Hospitalization for Community-Acquired Pneumonia in France. JAMA Network Open 2022;5(4):e226574 View
  5. Sandhu N, Krusina A, Quan H, Walker R, Martin E, Eastwood C, Southern D. Automated extraction of weight, height, and obesity in electronic medical records are highly valid. Obesity Science & Practice 2024;10(1) View
  6. He S, Park S, Kuklina E, Therrien N, Lundeen E, Wall H, Lampley K, Kompaniyets L, Pierce S, Sperling L, Jackson S. Leveraging Electronic Health Records to Construct a Phenotype for Hypertension Surveillance in the United States. American Journal of Hypertension 2023;36(12):677 View
  7. Walling A, Pevnick J, Bennett A, Vydiswaran V, Ritchie C. Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research. Journal of the American Medical Informatics Association 2023;30(7):1333 View
  8. Pan J, Zhang Z, Peters S, Vatanpour S, Walker R, Lee S, Martin E, Quan H. Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing. Brain Informatics 2023;10(1) View
  9. Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg B, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data. Nature Communications 2023;14(1) View
  10. Lee S, Martin E, Pan J, Eastwood C, Southern D, Campbell D, Shaheen A, Quan H, Butalia S. Exploring the reliability of inpatient EMR algorithms for diabetes identification. BMJ Health & Care Informatics 2023;30(1):e100894 View
  11. Atiomo W, Rizwan M, Bajwa M, Furniturewala H, Hazari K, Harab D, Abdelkareem W, Inuwa S, Khamis A, Tahlak M, Mirza F. Prevalence and Diagnosis of PCOS Using Electronic Health Records: A Scoping Review and a Database Analysis. International Journal of Environmental Research and Public Health 2024;21(3):354 View
  12. Petit-Jean T, Gérardin C, Berthelot E, Chatellier G, Frank M, Tannier X, Kempf E, Bey R. Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions. Journal of the American Medical Informatics Association 2024;31(6):1280 View
  13. Stransky M, Bremer-Kamens M, Kistin C, Sheldrick R, Cohen R. Using Electronic Health Records to Identify Asthma-Related Acute Care Encounters. Academic Pediatrics 2024;24(8):1229 View
  14. Grothman A, Ma W, Tickner K, Martin E, Southern D, Quan H. Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review. JMIR Medical Informatics 2024;12:e49781 View
  15. Guo Q, Fu B, Tian Y, Xu S, Meng X. Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development. Current Medical Research and Opinion 2024;40(9):1483 View