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

Natural Language Processing (639) Clinical Information and Decision Making (1323) Clinical Informatics (911) Electronic Health Records (966) Tools, Programs and Algorithms (242) Secondary Use of Clinical Data for Research and Surveillance (319) Ontologies, Classifications, and Coding (224)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23983, first published .
A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study

A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study

A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study

Authors of this article:

Jimyung Park1 Author Orcid Image ;   Seng Chan You2 Author Orcid Image ;   Eugene Jeong3 Author Orcid Image ;   Chunhua Weng4 Author Orcid Image ;   Dongsu Park5 Author Orcid Image ;   Jin Roh6 Author Orcid Image ;   Dong Yun Lee5 Author Orcid Image ;   Jae Youn Cheong7 Author Orcid Image ;   Jin Wook Choi8 Author Orcid Image ;   Mira Kang9 Author Orcid Image ;   Rae Woong Park1, 5 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Almeida J, Silva J, Matos S, Oliveira J. A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases. Journal of Biomedical Informatics 2021;120:103849 View
  2. Lee D, Kim C, Lee S, Son S, Cho S, Cho Y, Lim J, Park R. Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods. Frontiers in Psychiatry 2022;13 View
  3. Dobbins N, Mullen T, Uzuner Ö, Yetisgen M. The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria. Scientific Data 2022;9(1) View
  4. Kim H, Park Y, Park Y, Choi E, Kim S, You H, Bae Y. Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers. IEEE Access 2023;11:16066 View
  5. Bhatt S, Johnson P, Markovitz N, Gray T, Nipp R, Ufere N, Rice J, Reynolds M, Lavoie M, Clay M, Lindvall C, El-Jawahri A. The Use of Natural Language Processing to Assess Social Support in Patients With Advanced Cancer. The Oncologist 2023;28(2):165 View
  6. Park C, You S, Jeon H, Jeong C, Choi J, Park R. Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data. Yonsei Medical Journal 2022;63(Suppl):S74 View
  7. Ciampi M, Sicuranza M, Silvestri S. A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data. Information 2022;13(2):87 View
  8. Lee D, Kim N, Park C, Gan S, Son S, Park R, Park B. Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing. Psychiatry Research 2024;334:115817 View
  9. Loor-Torres R, Duran M, Toro-Tobon D, Mateo Chavez M, Ponce O, Soto Jacome C, Segura Torres D, Algarin Perneth S, Montori V, Golembiewski E, Borras Osorio M, Fan J, Singh Ospina N, Wu Y, Brito J. A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology. Mayo Clinic Proceedings: Digital Health 2024;2(2):270 View