Using MEDLINE Elemental Similarity to Assist in the Article Screening Process for Systematic Reviews

JMIR Medical Informatics
Xiaonan Ji, Po-Yin Yen

Abstract

Systematic reviews and their implementation in practice provide high quality evidence for clinical practice but are both time and labor intensive due to the large number of articles. Automatic text classification has proven to be instrumental in identifying relevant articles for systematic reviews. Existing approaches use machine learning model training to generate classification algorithms for the article screening process but have limitations. We applied a network approach to assist in the article screening process for systematic reviews using predetermined article relationships (similarity). The article similarity metric is calculated using the MEDLINE elements title (TI), abstract (AB), medical subject heading (MH), author (AU), and publication type (PT). We used an article network to illustrate the concept of article relationships. Using the concept, each article can be modeled as a node in the network and the relationship between 2 articles is modeled as an edge connecting them. The purpose of our study was to use the article relationship to facilitate an interactive article recommendation process. We used 15 completed systematic reviews produced by the Drug Effectiveness Review Project and demonstrated the use of article...Continue Reading

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Citations

Dec 24, 2018·Ground Water·Franklin W SchwartzMotomu Ibaraki
Dec 7, 2019·BMC Medical Informatics and Decision Making·Austin J BrockmeierSophia Ananiadou
Jan 1, 2020·Tropical Medicine and Health·Yiding ZhangFranklin W Schwartz
Jan 9, 2020·Journal of Biomedical Informatics·Yiding ZhangFranklin W Schwartz

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