Use of topic modeling to assess research trends in the journal Gynecologic Oncology.

ABSTRACT

STUDY OBJECTIVE: There is scant research identifying thematic trends within medical research. This work may provide insight into how a given field values certain topics. We assessed the feasibility of using a machine learning approach to determine the most common research themes published in Gynecologic Oncology over a thirty-year period and to subsequently evaluate how interest in these topics changed over time. METHODS: We retrieved the abstracts of all original research published in Gynecologic Oncology from 1990 to 2020 using PubMed. Abstract text was processed through a natural language processing algorithm and clustered into topical themes using latent Dirichlet allocation (LDA) prior to manual labeling. Topics were investigated for temporal trends. RESULTS: We retrieved 12,586 original research articles, of which 11,217 were evaluable for subsequent analysis. Twenty-three research topics were selected at the completion of topic modeling. The topics of basic science genetics, epidemiologic methods, and chemotherapy experienced the greatest increase over the time period, while postoperative outcomes, reproductive age cancer management, and cervical dysplasia experienced the greatest decline. Interest in basic science research remained relatively constant. Topics were additionally reviewed for words indicative of either surgical or medical therapy. Both surgical and medical topics saw increasing interest, with surgical topics experiencing a greater increase and representing a higher proportion of published topics. CONCLUSIONS: Topic modeling, a type of unsupervised machine learning, was successfully used to identify trends in research themes. The application of this technique provided insight into how the field of gynecologic oncology values the components of its scope of practice and therefore how it may choose to allocate grant funding, disseminate research, and participate in the public discourse.