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.

翻译：科学研究趋势与热点随时间不断演变。识别并预测这些趋势对于教育机构、从业者、投资者及资助组织至关重要。本研究利用多源异构数据预测科学出版物未来趋势，包括来自PubMed的历史出版物时间序列、研究与综述文章、预训练语言模型及专利数据。我们证明，在40年跨度内，针对125个涵盖生命科学概念、生物医学、解剖学及其他科学、技术与工程领域的多样化主题，可提前五年预测科学主题的流行度水平及其变化趋势（即趋势本身）。前期出版物与未来专利是新兴科学主题的领先指标。研究发现，综述文章与原创研究论文的比例为识别主题兴衰提供重要信息——处于衰退期主题的综述文章占比偏高。语言模型能更深入地洞察时间动态并提供更优预测。在时间验证中，本模型显著优于历史基线方法。研究结果表明，类似的动态规律同样适用于其他科学与工程研究领域。