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; Mandy Korpusik; Sai Das; Cheryl Gilhooly; Ryan Simpson; James Glass; Susan Roberts

doi:10.2196/26988

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
, Mandy Korpusik
, Sai Das
, Cheryl Gilhooly
, Ryan Simpson
, James Glass
, Susan Roberts

Computer Science

Tufts University
Massachusetts Institute of Technology

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	e26988
Journal	Journal of Medical Internet Research
Volume	23
Issue number	12
DOIs	https://doi.org/10.2196/26988
State	Published - Dec 2021

ASJC Scopus Subject Areas

Health Informatics

Keywords

24-hour recall
App
Convolutional neural networks
Diet
Energy intake
Machine learning
Macronutrient intakes
Natural language processing
Nutrition

Access to Document

10.2196/26988

Cite this

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. / Taylor, Salima; Korpusik, Mandy; Das, Sai et al.
In: Journal of Medical Internet Research, Vol. 23, No. 12, e26988, 12.2021.

Research output: Contribution to journal › Article › peer-review

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AU - Gilhooly, Cheryl

AU - Simpson, Ryan

AU - Glass, James

AU - Roberts, Susan

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KW - Energy intake

KW - Machine learning

KW - Macronutrient intakes

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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

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this