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

Reviews in Medical Informatics (114) Natural Language Processing (919) Clinical Information and Decision Making (1927) mHealth in a Clinical Setting (744) Machine Learning (2083) Information Retrieval (348) Electronic Health Records (1322) Clinical Cancer Research (118) Health Services Research in Oncology (60)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68707, first published .
Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review

Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review

Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review

Authors of this article:

Simon Dahl1, 2 Author Orcid Image ;   Martin Bøgsted1, 2, 3 Author Orcid Image ;   Tomer Sagi4 Author Orcid Image ;   Charles Vesteghem1, 2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Gurupur V, Hooshmand S, Prabhu D, Trader E, Salvi S. Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare. Healthcare 2025;13(22):2900 View
  2. May P, Greß J, Seidel C, Sommer S, Schuler M, Nokodian S, Schröder F, Jung J. Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data. JMIR Medical Informatics 2025;13:e78332 View