基于条件层规范化的联合提取系统,用于健康监测
Binbin Shi1, Rongli Fan1, Lijuan Zhang2
1School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种新的人工智能模型,用于使用自然语言处理 (NLP) 进行健康监测. 该模型改进了关系三重提取,这对于准确的健康数据分析至关重要.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理 (NLP) 是一种自然语言处理.
- 生物医学信息学 生物医学信息学
背景情况:
- 自然语言处理 (NLP) 对健康监测至关重要,关系三重提取是关键组成部分.
- 当前的方法在准确提取复杂或重叠的信息方面面临挑战.
研究的目的:
- 在健康监测中提出联合实体和关系提取的新模式.
- 为了提高重叠的三重提取的准确性,使用NLP.
主要方法:
- 一个新的模型结合了条件层正常化和说话头注意力机制.
- 整合位置信息以提高提取精度.
- 对百度2019和CHIP2020数据集的评估.
主要成果:
- 拟议的模型有效地提取重叠的三胞胎.
- 与基线方法相比,观察到显著的性能改善.
- 实体识别和关系提取之间的增强互动.
结论:
- 这种新型模型在健康监测的关系三重提取方面表现出卓越的性能.
- 这一进步有助于更准确,更有效地分析健康数据.
- 这种方法对改善人工智能驱动的健康监测系统充满希望.
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