Automatic identification of recent high impact clinical articles in PubMed to support clinical decision making using time-agnostic features.

ABSTRACT:

Objectives

Finding recent clinical studies that warrant changes in clinical practice ("high impact" clinical studies) in a timely manner is very challenging. We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance.

Methods

To identify recent studies, we developed our classification model using time-agnostic features that are available as soon as an article is indexed in PubMed®, such as journal impact factor, author count, and study sample size. Using a gold standard of 541 high impact treatment studies referenced in 11 disease management guidelines, we tested the following null hypotheses: (1) the high impact classifier with time-agnostic features (HI-TA) performs equivalent

SUBMITTER: Bian J 

PROVIDER: S-EPMC6342626 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature