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Natural language processing of spoken diet records (SDRs).

Authors: Ronilda C. Lacson; William J. Long;

Natural language processing of spoken diet records (SDRs).

Abstract

Dietary assessment is a fundamental aspect of nutritional evaluation that is essential for management of obesity as well as for assessing dietary impact on chronic diseases. Various methods have been used for dietary assessment including written records, 24-hour recalls, and food frequency questionnaires. The use of mobile phones to provide real-time dietary records provides potential advantages for accessibility, ease of use and automated documentation. However, understanding even a perfect transcript of spoken dietary records (SDRs) is challenging for people. This work presents a first step towards automatic analysis of SDRs. Our approach consists of four steps - identification of food items, identification of food quantifiers, classification of food quantifiers and temporal annotation. Our method enables automatic extraction of dietary information from SDRs, which in turn allows automated mapping to a Diet History Questionnaire dietary database. Our model has an accuracy of 90%. This work demonstrates the feasibility of automatically processing SDRs.

Related Organizations
Keywords

Electronic Data Processing, Surveys and Questionnaires, Humans, Speech, Algorithms, Diet Records, Natural Language Processing

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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Top 10%
Average
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