Boosting efficiency in a clinical literature surveillance system with LightGBM.

ABSTRACT: Given the suboptimal performance of Boolean searching to identify methodologically sound and clinically relevant studies in large bibliographic databases, exploring machine learning (ML) to efficiently classify studies is warranted. To boost the efficiency of a literature surveillance program, we used a large internationally recognized dataset of articles tagged for methodological rigor and applied an automated ML approach to train and test binary classification models to predict the probability of clinical research articles being of high methodologic quality. We trained over 12,000 models on a dataset of titles and abstracts of 97,805 articles indexed in PubMed from 2012-2018 which were manually appraised for rigor by highly trained research associates and rated for clinical relevancy by

SUBMITTER: Lokker C 

PROVIDER: S-EPMC11419392 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature