Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study

Abstract

To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing.

Document Details

• Document Type: Pub Defense Publication
• Publication Date: Sep 23, 2020
• Source ID: 10.1111/1475-6773.13560

Entities

People

• Andrew J Karter
• Danielle S. McNamara
• Dean Schillinger
• Jennifer Y. Liu
• Renu Balyan
• Scott A. Crossley

Organizations

• Arizona State University
• Eunice Kennedy Shriver National Institute of Child Health and Human Development
• Georgia State University
• Institute of Education Sciences
• National Institute of Diabetes and Digestive and Kidney Diseases
• National Institutes of Health
• Office of Naval Research
• University of California, San Francisco

Tags

Readers

• Computational Linguistics
• Distributed Systems and Data Platform Development
• Mental Health of Military Veterans with Posttraumatic Stress Disorder (PTSD): Risk Factors, Prevalence, Symptoms, and Treatment.

Technology Areas

• AI & ML
• AI & ML - Machine Translation
• AI & ML - Neural Networks