Data from: Trends in anesthesiology research: a machine learning approach to theme discovery and summarization

Objectives: Traditionally, summarization of research themes and trends
within a given discipline was accomplished by manual review of scientific
works in the field. However, with the ushering in of the age of “big
data”, new methods for discovery of such information become necessary as
traditional techniques become increasingly difficult to apply due to the
exponential growth of document repositories. Our objectives are to develop
a pipeline for unsupervised theme extraction and summarization of thematic
trends in document repositories, and to test it by applying it to a
specific domain. Methods: To that end, we detail a pipeline, which
utilizes machine learning and natural language processing for unsupervised
theme extraction, and a novel method for summarization of thematic trends,
and network mapping for visualization of thematic relations. We then apply
this pipeline to a collection of anesthesiology abstracts. Results: We
demonstrate how this pipeline enables discovery of major themes and
temporal trends in anesthesiology research and facilitates document
classification and corpus exploration. Discussion: The relation of
prevalent topics and extracted trends to recent events in both
anesthesiology, and healthcare in general, demonstrates the pipeline’s
utility. Furthermore, the agreement between the unsupervised thematic
grouping and human-assigned classification validates the pipeline’s
accuracy and demonstrates another potential use. Conclusion: The described
pipeline enables summarization and exploration of large document
repositories, facilitates classification, aids in trend identification. A
more robust and user-friendly interface will facilitate the expansion of
this methodology to other domains. This will be the focus of future work
for our group.