Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26988, first published .
Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Salima Taylor   1 * , MS ;   Mandy Korpusik   2 * , PhD ;   Sai Das   1 , PhD ;   Cheryl Gilhooly   1 , PhD ;   Ryan Simpson   3 , MS ;   James Glass   2 , PhD ;   Susan Roberts   1 , PhD

1 Jean Mayer United States Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States

2 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

3 Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States

*these authors contributed equally

Corresponding Author:

  • Susan Roberts, PhD
  • Jean Mayer United States Department of Agriculture Human Nutrition Research Center on Aging
  • Tufts University
  • 711 Washington Street
  • Boston, MA, 02111
  • United States
  • Phone: 1 617-556-3238
  • Email: susan.roberts@tufts.edu