在深度学习中平衡模型复杂性和临床部署性,以提取社会人口统计信息.
Rawan Abulibdeh1, Karen Tu2,3,4, Ervin Sejdić1,4
1Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
Journal of primary care & community health
|December 17, 2025
概括
简单的卷积神经网络 (CNN) 模型比复杂或混合模型更有效地从电子医疗记录 (EMR) 文本中提取社会人口统计因素. 这一发现有助于开发有效的自然语言处理 (NLP) 用于健康公平研究.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 社会人口统计学因素显著影响健康结果和差异.
- 电子医疗记录 (EMR) 通常在非结构化文本中对这些因素的文档很少.
- 自动提取对临床决策和健康公平研究具有挑战性.
研究的目的:
- 系统地评估和比较六个卷积神经网络 (CNN) 架构,从EMR文本中对社会人口特征进行分类.
- 评估模型复杂性和词汇多样性对分类性能的影响.
- 为有效和可解释的临床NLP管道确定最佳模型.
主要方法:
- 利用了来自96个初级保健诊所的4375名患者的数据.
- 采用六个CNN架构,包括混合模型,用于二进制分类任务.
- 使用F1分数,精度,回忆,AUC-PR和马修斯相关系数评估性能,在手动注释中具有高的评级者间可靠性.
主要成果:
- 更简单的架构,特别是单层CNN,在大多数特征上始终优于更深层或混合模型 (F1得分:90.99%).
- 更简单的模型在数据不平衡和各种文档模式下显示出更高的性能.
- 混合模型对记录良好的因素更有效,但对稀疏或多样化的特征更不有效.
结论:
- 简单的CNN架构为开发高效的临床NLP管道开发社会人口统计数据提取提供了实际框架.
- 结果为现实世界健康公平和EMR研究应用的模型选择提供了信息.
- 该研究强调了在不同EMR数据环境中模型复杂性和性能之间的权衡.
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