Fast and Accurate Approaches for Large-Scale, Automated Mapping of Food Diaries on Food Composition Tables.

ABSTRACT: Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs. Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier

SUBMITTER: Lamarine M 

PROVIDER: S-EPMC5954085 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature