基于BERT的模型,用于有效检测电子健康记录中的异常
Haoran Niu1, Olufemi A Omitaomu1, Michael A Langston2
1University of Tennessee, Knoxville, Knoxville, TN, 37996, United States; Oak Ridge National Laboratory, Oak Ridge, TN, 37831, United States.
Journal of biomedical informatics
|February 8, 2024
概括
一个新的框架,EHR-双向编码器表示从变压器 (BERT),增强异常检测在电子健康记录 (EHRs). 这通过减少错误和提高医疗信息技术系统的可靠性来提高患者安全.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据分析 临床数据分析
背景情况:
- 医生依靠电子健康记录 (EHR) 来做出患者护理决策.
- 卫生信息技术 (HIT) 系统必须可靠,以确保患者的安全.
- 现有的深度学习方法用于电子病历异常检测具有局限性,包括高假负值和计算成本.
研究的目的:
- 提出一个新的框架,EHR-双向编码器变压器表示 (BERT),用于检测EHR中的异常.
- 解决当前深度学习方法在EHR异常检测方面的局限性.
- 提高EHR数据的可靠性,以提高患者安全.
主要方法:
- 在EHR-BERT框架中,使用了顺序掩盖令牌预测 (SMTP) 方法.
- 在训练和预测过程中,EHR被视为自然语言句子,代币在训练和预测过程中被掩盖.
- 该模型学习双向EHR序列模式,以根据正常模型的偏差识别异常.
主要成果:
- 在大型EHR数据集上,EHR-BERT显示了与现有模型相比的显著改进.
- 该框架大大减少了假阳性结果,并提高了异常检测率.
- 性能改善归因于最小化信息丢失和最大化数据利用.
结论:
- 在减少与异常临床事件相关的医疗错误方面,EHR-BERT显示出巨大的潜力.
- 该框架提高了患者安全和医疗保健服务的质量.
- EHR-BERT为确保健康数据的可靠性和质量提供了一个有前途的解决方案.
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