Revealing Academic Evolution and Frontier Pattern in the Field of Uveitis Using Bibliometric Analysis, Natural Language Processing, and Machine Learning

Numerous uveitis articles were published in this century, underneath which hides valuable intelligence. We aimed to characterize the evolution and patterns in this field.
We divided the 15,994 uveitis papers into four consecutive time periods for bibliometric analysis, and applied latent Dirichlet allocation topic modeling and machine learning techniques to the latest period. 
The yearly publication pattern fitted the curve: 1.21335x2 − 4,848.95282x + 4,844,935.58876 (R2 = 0.98311). The USA, the most productive country/region, focused on topics like ankylosing spondylitis and biologic therapy, whereas China (mainland) focused on topics like OCT and Behcet disease. The logistic regression showed the highest accuracy (71.6%) in the test set.
In this century, a growing number of countries/regions/authors/journals are involved in the uveitis study, promoting the scientific output and thematic evolution. Our pioneering study uncovers the evolving academic trends and frontier patterns in this field using bibliometric analysis and AI algorithms.