Extracting the Sample Size From Randomized Controlled Trials in Explainable Fashion Using Natural Language Processing

Background: Extracting the sample size from randomized controlled trials (RCTs) remains a challenge to developing better search functionalities or automating systematic reviews. Most current approaches rely on the sample size being explicitly mentioned in the abstract. Methods: 847 RCTs from high-impact medical journals were tagged with six different entities that could indicate the sample size. A named entity recognition (NER) model was trained to extract the entities and then deployed on a test set of 150 RCTs. The entities' performance in predicting the actual number of trial participants who were randomized was assessed and possible combinations of the entities were evaluated to create predictive models. Results: The most accurate model could make predictions for 64.7% of trials in the test set, and the resulting predictions were within 10% of the ground truth in 96.9% of cases. A less strict model could make a prediction for 96.0% of trials, and its predictions were within 10% of the ground truth in 88.2% of cases. Conclusion: Training a named entity recognition model to predict the sample size from randomized controlled trials is feasible, not only if the sample size is explicitly mentioned but also if the sample size can be calculated, e.g., by adding up the number of patients in each arm.