Machine learning to increase the efficiency of a literature surveillance system: a performance evaluation

BackgroundGiven suboptimal performance of Boolean searching to identify methodologically sound and clinically relevant studies in large bibliographic databases such as MEDLINE, exploring the performance of machine learning (ML) tools is warranted. ObjectiveUsing a large internationally recognized dataset of articles tagged for methodological rigor, we trained and tested binary classification models to predict the probability of clinical research articles being of high methodologic quality to support a literature surveillance program. Materials and MethodsUsing an automated machine learning approach, over 12,000 models were trained on a dataset of 97,805 articles indexed in PubMed from 2012-2018 which were manually appraised for rigor by highly trained research associates with expertise in research methods and critical appraisal. As the dataset is unbalanced, with more articles that do not meet criteria for rigor, we used the unbalanced dataset and over- and under-sampled datasets. Models that maintained sensitivity for high rigor at 99% and maximized specificity were selected and tested in a retrospective set of 30,424 articles from 2020 and validated prospectively in a blinded study of 5253 articles. ResultsThe final selected algorithm, combining a model trained in each dataset, maintained high sensitivity and achieved 57% specificity in the retrospective validation test and 53% in the prospective study. The number of articles needed to read to find one that met appraisal criteria was 3.68 (95% CI 3.52 to 3.85) in the prospective study, compared with 4.63 (95% CI 4.50 to 4.77) when relying only on Boolean searching. ConclusionsML models improved by approximately 25% the efficiency of detecting high quality clinical research publications for literature surveillance and subsequent dissemination to clinicians and other evidence users.