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

Data Science (357) Clinical Informatics (1325) Natural Language Processing (917) Electronic Health Records (1319) Clinical Information and Decision Making (1915) mHealth for Data Collection and Research (1196) Big Data (307) Machine Learning (2076) Information Retrieval (348) Health Information Exchange (146)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66910, first published .
Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study

Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study

Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study

Authors of this article:

Tom M Seinen1 Author Orcid Image ;   Jan A Kors1 Author Orcid Image ;   Erik M van Mulligen1 Author Orcid Image ;   Peter R Rijnbeek1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Liu L, Blake V, Barman M, Gallego B, Churches T, Kennedy G, Ooi S, Delaney G, Jorm L. Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review. Journal of the American Medical Informatics Association 2025 View
  2. Far A, Ayati A, Guillot J, Azzam S, Rudrapatna V, Ge J. Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation. Digestive Diseases and Sciences 2025 View
  3. Wang J, Lin A, Huang Y, Li G, Chen T, Sun C, Qian W, Ren S, Wong H, Ding Y, Zhang L. Medical Data as a Key Asset in the Digital Health Era: A Framework for Challenges and Strategies. iMetaMed 2025 View