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

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Natural Language Processing (859) Machine Learning (1934) Clinical Informatics (1241) Artificial Intelligence (1964)

Published on in Vol 10, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38095, first published .
Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning–Based Text Simplification Approach

Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning–Based Text Simplification Approach

Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning–Based Text Simplification Approach

Authors of this article:

Atharva Phatak1 Author Orcid Image ;   David W Savage2 Author Orcid Image ;   Robert Ohle3 Author Orcid Image ;   Jonathan Smith2 Author Orcid Image ;   Vijay Mago1 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

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  1. Swanson K, He S, Calvano J, Chen D, Telvizian T, Jiang L, Chong P, Schwell J, Mak G, Lee J, Tariq A. Biomedical text readability after hypernym substitution with fine-tuned large language models. PLOS Digital Health 2024;3(4):e0000489 View
  2. Breve B, Cimino G, Deufemia V. Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation Rules. ACM Transactions on Intelligent Systems and Technology 2024 View
  3. Liu Y, Wang H, Zhou H, Li M, Hou Y, Zhou S, Wang F, Hoetzlein R, Zhang R. A review of reinforcement learning for natural language processing and applications in healthcare. Journal of the American Medical Informatics Association 2024;31(10):2379 View
  4. Rahman M, Irbaz M, North K, Williams M, Zampieri M, Lybarger K. Health text simplification: An annotated corpus for digestive cancer education and novel strategies for reinforcement learning. Journal of Biomedical Informatics 2024;158:104727 View
  5. Nanyonga A, Wasswa H, Joiner K, Turhan U, Wild G. A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incidents. Modelling 2025;6(2):27 View
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