Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings

Abstract

Systematic reviews are a powerful tool to summarize the existing evidence in medical literature. However, identifying relevant articles is difficult, and this typically involves structured searches with keyword-based strategies, followed by the painstaking manual selection of relevant evidence. A.I. may help investigators, for example, through topic modeling, i.e., algorithms that can understand the content of a text. We applied BERTopic, a transformer-based topic-modeling algorithm, to two datasets consisting of 6137 and 5309 articles, respectively, used in recently published systematic reviews on peri-implantitis and bone regeneration. We extracted the title of each article, encoded it into embeddings, and input it into BERTopic, which then rapidly identified 14 and 22 topic clusters, respectively, and it automatically created labels describing the content of these groups based on their semantics. For both datasets, BERTopic uncovered a variable number of articles unrelated to the query, which accounted for up to 30% of the dataset—achieving a sensitivity of up to 0.79 and a specificity of at least 0.99. These articles could have been discarded from the screening, reducing the workload of investigators. Our results suggest that adding a topic-modeling step to the screening process could potentially save working hours for researchers involved in systematic reviews of the literature.