TY - JOUR
T1 - What's next? Forecasting scientific research trends
AU - Ofer, Dan
AU - Kaufman, Hadasah
AU - Linial, Michal
N1 - Publisher Copyright:
© 2023
PY - 2024/1/15
Y1 - 2024/1/15
N2 - 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 future publication trends for any topic covered in PubMed.
AB - 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 future publication trends for any topic covered in PubMed.
KW - Bibliometrics
KW - Citation analysis
KW - Machine learning
KW - MeSH
KW - NLP
KW - PubMed
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85180328114&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2023.e23781
DO - 10.1016/j.heliyon.2023.e23781
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C2 - 38223716
AN - SCOPUS:85180328114
SN - 2405-8440
VL - 10
JO - Heliyon
JF - Heliyon
IS - 1
M1 - e23781
ER -