Perspective: Towards Automated Tracking of Content and Evidence Appraisal of Nutrition Research.

ABSTRACT: Robust recommendations for healthy diets and nutrition require careful synthesis of available evidence. Given the increasing volume of research articles generated, the retrieval and synthesis of evidence are increasingly becoming laborious and time-consuming. Information technology could help to reduce workload for humans. To guide supervised learning however, human identification of key study characteristics is necessary. Reporting guidelines recommend that authors include essential content in articles and could generate manually labeled training data for automated evidence retrieval and synthesis. Here, we present a semiautomated approach to annotate, link, and track the content of nutrition research manuscripts. We used the STROBE extension for nutritional epidemiology (STROBE-nut) repo

SUBMITTER: Yang C 

PROVIDER: S-EPMC7490154 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature