Deep Neural Network for Reducing the Screening Workload in Systematic Reviews for Clinical Guidelines: Algorithm Validation Study.

ABSTRACT:

Background

Performing systematic reviews is a time-consuming and resource-intensive process.

Objective

We investigated whether a machine learning system could perform systematic reviews more efficiently.

Methods

All systematic reviews and meta-analyses of interventional randomized controlled trials cited in recent clinical guidelines from the American Diabetes Association, American College of Cardiology, American Heart Association (2 guidelines), and American Stroke Association were assessed. After reproducing the primary screening data set according to the published search strategy of each, we extracted correct articles (those actually reviewed) and incorrect articles (those not reviewed) from the data set. These 2 sets of articles were used to train a neural network

SUBMITTER: Yamada T 

PROVIDER: S-EPMC7806440 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature