Analyzing Alzheimer’s Disease Research Trends: Insights From Improved Dynamic Topic Modeling

Abstract

This study addresses the need for a comprehensive understanding of evolving research trends and challenges in Alzheimer’s disease research literature from 2016 to 2023. Employing an improved Dynamic Topic Model (DTM), we analyze the landscape from four critical perspectives: identifying predominant topics, analyzing variability in topic intensity, tracing evolutionary trajectories, and delineating development patterns of key research terms. Through an intensive investigation of four pivotal topics, we emphasize the imperative for sustained attention to these areas, crucial for guiding future research initiatives. Our findings reveal notable fluctuations in topic intensity, primarily attributed to nascent research domains lacking well-defined directions and cohesive research teams. Moreover, we observe a tendency for topics of high similarity to converge over time, signifying maturation and consolidation within the field. Importantly, our study underscores how focal points in Alzheimer’s disease research shift across developmental stages, shaped by dynamic interactions among the research community’s social dynamics, technological advancements, and evolving scientific priorities.

Keywords

• Alzheimer’s disease
• topic model
• topic mining
• trends analysis