Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews.

ABSTRACT:

Background

Evidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.

Methods

We evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies. Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models

SUBMITTER: Panayi A 

PROVIDER: S-EPMC10557215 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature