Automatic identification of high impact articles in PubMed to support clinical decision making.

ABSTRACT:

Objectives

The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians' retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta-analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed®.

Methods

Our machine learning algorithms use a variety of features including bibliometric features (e.g., citation count), social media attention, journal impact factors, and citation metadata. The algorithms were developed and evaluated with a gold standard composed of 502 high impact clinical studies that are referenc

SUBMITTER: Bian J 

PROVIDER: S-EPMC5583030 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature