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

Digital Pain Assessment and Management (150) Clinical Information and Decision Making (1328) Natural Language Processing (644) Decision Support for Health Professionals (1137) Electronic Health Records (966) Back Pain (103) Design and Usability of Clinical Software and EHRs (242) Clinical Informatics (912) Machine Learning (1385)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46857, first published .
Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study

Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study

Natural Language Processing of Referral Letters for Machine Learning–Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study

Authors of this article:

Sebastian Fudickar1, 2 Author Orcid Image ;   Carsten Bantel3, 4 Author Orcid Image ;   Jannik Spieker2 Author Orcid Image ;   Heinrich Töpfer2 Author Orcid Image ;   Patrick Stegeman5 Author Orcid Image ;   Henrica R Schiphorst Preuper5 Author Orcid Image ;   Michiel F Reneman5 Author Orcid Image ;   André P Wolff6 Author Orcid Image ;   Remko Soer6, 7 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Wang P, Zhang Z, Xie Z, Liu L, Ren G, Guo Z, Xu L, Yin X, Hu Y, Wang Y, Wu X. Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation. World Neurosurgery 2024 View