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

Development and Evaluation of Research Methods, Instruments and Tools (511) Artificial Intelligence (1421) Clinical Information and Decision Making (1434) Natural Language Processing (706) Machine Learning (1501) Methods and Instruments in Medical Informatics (135) Decision Support for Health Professionals (1210) Breast Cancer (213)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48534, first published .
Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation

Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation

Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation

Authors of this article:

Shinichi Matsuda1 Author Orcid Image ;   Takumi Ohtomo1 Author Orcid Image ;   Masaru Okuyama2 Author Orcid Image ;   Hiraku Miyake2 Author Orcid Image ;   Kotonari Aoki1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Nishioka S, Watabe S, Yanagisawa Y, Sayama K, Kizaki H, Imai S, Someya M, Taniguchi R, Yada S, Aramaki E, Hori S. Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models. Journal of Medical Internet Research 2024;26:e55794 View
  2. Balch J, Chatham A, Hong P, Manganiello L, Baskaran N, Bihorac A, Shickel B, Moseley R, Loftus T. Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor. Frontiers in Artificial Intelligence 2024;7 View