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Perspective : towards automated tracking of content and evidence appraisal of nutrition research

Chen Yang, Dana Hawwash, Bernard De Baets (UGent) , Jildau Bouwman and Carl Lachat (UGent)
(2020) ADVANCES IN NUTRITION. 11(5). p.1079-1088
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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) reporting guidelines to manually annotate a sample of 15 articles and converted the semantic information into linked data in a Neo4j graph database through an automated process. Six summary statistics were computed to estimate the reporting completeness of the articles. The content structure, presence of essential study characteristics as well as the reporting completeness of the articles are visualized automatically from the graph database. The archived linked data are interoperable through their annotations and relations. A graph database with linked data on essential study characteristics can enable Natural Language Processing in nutrition.
Keywords
STROBE-nut, reporting guidelines, graph database, research semantics, ontology, standardization, EPIDEMIOLOGY

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MLA
Yang, Chen, et al. “Perspective : Towards Automated Tracking of Content and Evidence Appraisal of Nutrition Research.” ADVANCES IN NUTRITION, vol. 11, no. 5, 2020, pp. 1079–88, doi:10.1093/advances/nmaa057.
APA
Yang, C., Hawwash, D., De Baets, B., Bouwman, J., & Lachat, C. (2020). Perspective : towards automated tracking of content and evidence appraisal of nutrition research. ADVANCES IN NUTRITION, 11(5), 1079–1088. https://doi.org/10.1093/advances/nmaa057
Chicago author-date
Yang, Chen, Dana Hawwash, Bernard De Baets, Jildau Bouwman, and Carl Lachat. 2020. “Perspective : Towards Automated Tracking of Content and Evidence Appraisal of Nutrition Research.” ADVANCES IN NUTRITION 11 (5): 1079–88. https://doi.org/10.1093/advances/nmaa057.
Chicago author-date (all authors)
Yang, Chen, Dana Hawwash, Bernard De Baets, Jildau Bouwman, and Carl Lachat. 2020. “Perspective : Towards Automated Tracking of Content and Evidence Appraisal of Nutrition Research.” ADVANCES IN NUTRITION 11 (5): 1079–1088. doi:10.1093/advances/nmaa057.
Vancouver
1.
Yang C, Hawwash D, De Baets B, Bouwman J, Lachat C. Perspective : towards automated tracking of content and evidence appraisal of nutrition research. ADVANCES IN NUTRITION. 2020;11(5):1079–88.
IEEE
[1]
C. Yang, D. Hawwash, B. De Baets, J. Bouwman, and C. Lachat, “Perspective : towards automated tracking of content and evidence appraisal of nutrition research,” ADVANCES IN NUTRITION, vol. 11, no. 5, pp. 1079–1088, 2020.
@article{8664145,
  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) reporting guidelines to manually annotate a sample of 15 articles and converted the semantic information into linked data in a Neo4j graph database through an automated process. Six summary statistics were computed to estimate the reporting completeness of the articles. The content structure, presence of essential study characteristics as well as the reporting completeness of the articles are visualized automatically from the graph database. The archived linked data are interoperable through their annotations and relations. A graph database with linked data on essential study characteristics can enable Natural Language Processing in nutrition.}},
  author       = {{Yang, Chen and Hawwash, Dana and De Baets, Bernard and Bouwman, Jildau and Lachat, Carl}},
  issn         = {{2161-8313}},
  journal      = {{ADVANCES IN NUTRITION}},
  keywords     = {{STROBE-nut,reporting guidelines,graph database,research semantics,ontology,standardization,EPIDEMIOLOGY}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{1079--1088}},
  title        = {{Perspective : towards automated tracking of content and evidence appraisal of nutrition research}},
  url          = {{http://doi.org/10.1093/advances/nmaa057}},
  volume       = {{11}},
  year         = {{2020}},
}
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