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

AI Language Models in Health Care (222) Natural Language Processing (934) Patient-Reported Outcome Measures (PROMs) (376) Development and Evaluation of Research Methods, Instruments and Tools (772) Methods and Instruments in Medical Informatics (205) Participatory Medicine & E-Patients (887) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1006)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63466, first published .
Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study

Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study

Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study

Authors of this article:

Brian Steele1 Author Orcid Image ;   Paul Fairie2, 3 Author Orcid Image ;   Kyle Kemp2 Author Orcid Image ;   Adam G D'Souza1, 4 Author Orcid Image ;   Matthias Wilms2, 5 Author Orcid Image ;   Maria Jose Santana2, 3, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Cheese E, Bichoo R, Grover K, Dumitru D, Zenonos A, Groark J, Gibson D, Pope R. Using Natural Language Processing to Explore Patient Perspectives on AI Avatars in Support Materials for Patients With Breast Cancer: Survey Study. Journal of Medical Internet Research 2025;27:e70971 View
  2. Chatzimina M, Papadaki H, Pontikoglou C, Tsiknakis M. Topic modeling and sentiment analysis of Greek clinician–patient conversations in hematologic malignancies. International Journal of Medical Informatics 2025;204:106071 View

Conference Proceedings

  1. Guo D, Luo Z, Bouguila N, Fan W. 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). BiovMNVTM: A Geometry-Aware Neural Topic Model for Biomedical Text Analysis via von Mises–Fisher Mixtures View