  Scientific research trends and interests evolve over time. The ability to
identify and forecast these trends is vital for educational institutions,
practitioners, investors, and funding organizations. In this study, we predict
future trends in scientific publications using heterogeneous sources, including
historical publication time series from PubMed, research and review articles,
pre-trained language models, and patents. We demonstrate that scientific topic
popularity levels and changes (trends) can be predicted five years in advance
across 40 years and 125 diverse topics, including life-science concepts,
biomedical, anatomy, and other science, technology, and engineering topics.
Preceding publications and future patents are leading indicators for emerging
scientific topics. We find the ratio of reviews to original research articles
informative for identifying increasing or declining topics, with declining
topics having an excess of reviews. We find that language models provide
improved insights and predictions into temporal dynamics. In temporal
validation, our models substantially outperform the historical baseline. Our
findings suggest that similar dynamics apply across other scientific and
engineering research topics. We present SciTrends, a user-friendly webtool for
predicting scientific topics trends:
https://hadasakaufman.shinyapps.io/SciTrend
