人工智能主题建模:在分析女性阿片类药物相关心血管风险时,比较LDA和BERTopic
Li Ma1,2, Ru Chen3, Weigong Ge1
1Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.
Experimental biology and medicine (Maywood, N.J.)
|March 17, 2025
概括
人工智能 (AI) 增强了分析女性处方阿片类药物心血管风险的主题建模. 与传统的隐性狄里克莱特分配 (LDA) 相比,人工智能辅助的BERTopic提供了更好的解释性和语义连贯性.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
- 计算语言学 计算语言学
背景情况:
- 传统的主题建模方法,如隐性迪里克莱特分配 (LDA),在捕获文本数据中的语义关系方面存在局限性.
- 基于深度学习的新技术BERTopic在理解语境词语关系方面表现出色.
- 处方阿片类药物使用对女性构成心血管风险,需要有效的文本分析方法.
研究的目的:
- 将人工智能 (AI) 模块集成到LDA和BERTopic中进行比较分析.
- 评估人工智能辅助主题建模在分析与女性处方阿片类药物相关的心血管风险方面的有效性.
- 将LDA和BERTopic在应用于生物医学摘要时的解释性和语义连贯性进行比较.
主要方法:
- 分析了1837篇关于"阿片类药物"",心血管"和"女性"的PubMed摘要.
- 隐藏的迪里克莱特分配 (LDA) 是使用机器学习语言工具包 (MALLET) 实现的.
- BERTopic与BioBERT一起用于文档嵌入,而ChatGPT-4-Turbo则用于主题解释.
主要成果:
- 当LDA和BERTopic被人工智能增强时,它们都显示出由专家审查验证的类似的性能准确性.
- 聊天GPT-4-Turbo为LDA和BERTopic生成了高度相关的主题描述.
- t 分布的静态邻居嵌入 (t-SNE) 图表显示了BERTopic的更紧和更明显的集群,这表明主题连贯性有所改善.
结论:
- 人工智能算法可以有效地增强传统和现代主题建模技术.
- 人工智能辅助的BERTopic提供了增强的可解释性和语义连贯性,超过了LDA,特别是在像ChatGPT-4-Turbo这样的集成大型语言模型中.
- 虽然LDA适用于资源有限的大规模分析,但人工智能辅助的BERTopic为复杂的文本数据分析提供了卓越的洞察力.
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