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

Electronic Health Records (1053) Machine Learning (1465) Natural Language Processing (692) Decision Support for Health Professionals (1185)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25157, first published .
Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study

Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study

Assessment of Natural Language Processing Methods for Ascertaining the Expanded Disability Status Scale Score From the Electronic Health Records of Patients With Multiple Sclerosis: Algorithm Development and Validation Study

Authors of this article:

Zhen Yang1 Author Orcid Image ;   Chloé Pou-Prom1 Author Orcid Image ;   Ashley Jones2 Author Orcid Image ;   Michaelia Banning1 Author Orcid Image ;   David Dai1 Author Orcid Image ;   Muhammad Mamdani1, 3, 4, 5, 6 Author Orcid Image ;   Jiwon Oh2, 4, 7 Author Orcid Image ;   Tony Antoniou1, 3, 8, 9 Author Orcid Image
Article Authors Cited by (10) Tweetations (13) Metrics

Journals

  1. Tabashum T, Xiao T, Jayaraman C, Mummidisetty C, Jayaraman A, Albert M. Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation. Bioengineering 2022;9(10):572 View
  2. Rodríguez-Fernández J, Loeb J, Hier D. It’s time to change our documentation philosophy: writing better neurology notes without the burnout. Frontiers in Digital Health 2022;4 View
  3. Swetlik C, Bove R, McGinley M. Clinical and Research Applications of the Electronic Medical Record in Multiple Sclerosis: A Narrative Review of Current Uses and Future Applications. International Journal of MS Care 2022;24(6):287 View
  4. Howlett-Prieto Q, Oommen C, Carrithers M, Wunsch D, Hier D. Subtypes of relapsing-remitting multiple sclerosis identified by network analysis. Frontiers in Digital Health 2023;4 View
  5. Kurvits S, Harro A, Reigo A, Ott A, Laur S, Särg D, Tampuu A, Alasoo K, Vilo J, Milani L, Haller T. Common clinical blood and urine biomarkers for ischemic stroke: an Estonian Electronic Health Records database study. European Journal of Medical Research 2023;28(1) View
  6. Muros-Le Rouzic E, Ghiani M, Zhuleku E, Dillenseger A, Maywald U, Wilke T, Ziemssen T, Craveiro L. Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records. Frontiers in Neurology 2023;14 View
  7. Demuth S, Paris J, Faddeenkov I, De Sèze J, Gourraud P. Clinical applications of deep learning in neuroinflammatory diseases: A scoping review. Revue Neurologique 2024 View
  8. Lefkovitz I, Walsh S, Blank L, Jetté N, Kummer B. Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review. JMIR Neurotechnology 2024;3:e51822 View
  9. Wieland-Jorna Y, van Kooten D, Verheij R, de Man Y, Francke A, Oosterveld-Vlug M. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open 2024;7(2) View
  10. Nunes M, Bone J, Ferreira J, Elvas L. Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review. JMIR Medical Informatics 2024;12:e60164 View