电子健康记录主题建模技术的系统审查
Iqra Mehmood1, Zoya Zahra1, Sarah Iqbal2
1Department of Computer and Information Sciences, PIEAS, Lehtrar Road, Nilore, Islamabad 45650, Pakistan.
Healthcare (Basel, Switzerland)
|January 28, 2026
概括
主题建模有效地分析电子健康记录 (EHR),提取有价值的临床见解. 本综述综合了对EHR数据的时间主题建模方面的进展,强调了人工智能集成的未来潜力.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
背景情况:
- 电子健康记录 (EHR) 提供了丰富的临床数据,但由于规模,异质性和时间复杂性而存在分析挑战.
- 主题建模,特别是基于神经和变压器的先进方法,如BERTopic,增强了从EHR中提取潜在结构和患者轨迹.
- 传统的概率模型和新的神经嵌入技术正在适应EHR分析.
研究的目的:
- 在过去十年中对EHR数据应用的主题建模技术进行系统文献综述 (SLR).
- 分析出版的趋势,数据集的使用,应用领域和EHR主题建模中的方法论进步.
- 在EHR数据的背景下,确定各种主题建模方法的优点和挑战.
主要方法:
- 遵守系统审查和元分析 (PRISMA) 框架的首选报告项目,用于研究选择.
- 审查主题建模技术,包括概率模型,神经嵌入方法和时间扩展.
- 对研究的分析,重点是临床数据中的途径和序列建模.
主要成果:
- 综合出版趋势,共同的数据集,以及在EHR中主题建模的各种应用领域.
- 识别不同建模家族的连贯性,可扩展性和域名适应性方面的优势.
- 突出在扩展性,解释性,时间复杂性和数据隐私方面的持续挑战,用于大规模的EHR分析.
结论:
- 主题建模对于发现EHR数据中的时间模式和潜在结构至关重要.
- 未来的方向包括将主题建模与Agentic AI和大型语言模型集成在一起,以改善临床决策.
- 这个SLR为研究人员和从业人员提供了一个基本的资源,用于推进数据驱动医疗保健的时间主题建模.
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