在临床文本中预测实体修饰者的多任务转移学习:应用到阿片类药物使用障碍病例检测中的应用
Abdullateef I Almudaifer1,2, Whitney Covington3, JaMor Hairston3
1University of Alabama at Birmingham, 1720 University Blvd, Birmingham, 35294, Alabama, USA. lateef11@uab.edu.
Journal of biomedical semantics
|June 7, 2024
概括
本研究引入了一种多任务变压器模型,用于准确识别临床实体修饰器,提高临床文本分析的准确性,并为新数据集提供有效的转移学习.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 机器学习 机器学习
背景情况:
- 临床文本语义受到否定,不确定性和严重性等修饰者的显著影响.
- 目前用于修饰器检测的方法通常依赖于独立的,基于特征的方法.
研究的目的:
- 开发和评估一个多任务变压器架构,用于联合学习临床实体修饰器.
- 用共享和新型数据集评估临床修饰剂转移学习的有效性.
主要方法:
- 设计了一种多任务变压器架构,以共同学习和预测临床实体修饰器.
- 该模型在SemEval 2015任务14集体和新的阿片类药物使用障碍 (OUD) 数据集上进行了训练和评估.
- 性能与现有系统进行了比较,并评估了转移学习的可行性.
主要成果:
- 提出的方法在SemEval 2015任务14 ShARe集体上取得了最先进的表现.
- 观察到显著的改善:加权精度为1.1%,未加权精度为1.7%,微型F1得分为10%.
- 该模型证明了临床文本修改器的有效转移学习能力.
结论:
- 从共享多任务模型中学习的权重有效地转移到一个新的,部分匹配的数据集.
- 这证实了转移学习对于增强临床实体修饰器检测的实用性.
- 这些发现支持开发更强大的临床NLP工具.
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