Published on in Vol 22, No 5 (2020): May

This is a member publication of Florida State University

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16795, first published .
An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study

An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study

An Informatics Framework to Assess Consumer Health Language Complexity Differences: Proof-of-Concept Study

Authors of this article:

Biyang Yu1 Author Orcid Image ;   Zhe He1 Author Orcid Image ;   Aiwen Xing2 Author Orcid Image ;   Mia Liza A Lustria1 Author Orcid Image

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  2. Gurupur V, Shelleh M. Machine Learning Analysis for Data Incompleteness (MADI): Analyzing the Data Completeness of Patient Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records. IEEE Access 2021;9:95994 View
  3. Alekhya B, Sasikumar R. An ensemble approach for healthcare application and diagnosis using natural language processing. Cognitive Neurodynamics 2022;16(5):1203 View
  4. Gurupur V, Abedin P, Hooshmand S, Shelleh M. Analyzing the Data Completeness of Patients’ Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records. Applied Sciences 2022;12(21):10746 View
  5. Odigie E, Andreadis K, Chandra I, Mocchetti V, Rives H, Cox S, Rameau A. Are Mobile Applications in Laryngology Designed for All Patients?. The Laryngoscope 2023;133(7):1540 View
  6. Abd-Elsayed A, Marcondes L, Loris Z, Reilly D. Painful Diabetic Peripheral Neuropathy – A Survey of Patient Experiences. Journal of Pain Research 2023;Volume 16:2269 View
  7. Babaali M, Fatemi A, Nematbakhsh M, Blake J. Creating and validating the Fine-Grained Question Subjectivity Dataset (FQSD): A new benchmark for enhanced automatic subjective question answering systems. PLOS ONE 2024;19(5):e0301696 View