将编码疾病序列的自然语言处理表示进行比较,以便在电子健康记录中进行预测
Thomas Beaney1,2, Sneha Jha2, Asem Alaa2
1Department of Primary Care and Public Health, Imperial College London, London, W12 0BZ, United Kingdom.
概括
基于序列的自然语言处理 (NLP) 算法,特别是像EHR-BERT这样的变压器模型,显示出电子健康记录 (EHR) 数据对临床结果的预测比袋子词的方法更好.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 机器学习在医疗保健中的应用
背景情况:
- 自然语言处理 (NLP) 算法越来越多地用于电子健康记录 (EHR) 数据的无监督表示.
- 不同NLP算法的比较性能用于从EHR预测临床终点并未得到充分确立.
研究的目的:
- 为了比较来自词袋的无监督表示与基于序列的NLP算法的性能.
- 通过使用EHR数据,评估这些表示对临床结果的预测准确性.
主要方法:
- 利用了来自英格兰600多万个人的初级保健EHR数据.
- 使用潜伏迪里克莱特分配,doc2vec和变压器模型生成时间顺序疾病序列的无监督向量表示.
- 开发了EHR-BERT,这是一个包含社会人口统计数据的变压器模型.
- 对一年死亡率,医疗保健使用和新诊断的预测性表现进行比较.
主要成果:
- 基于序列的NLP算法在预测临床终点方面始终超过了词包方法.
- 在所有预测任务中,EHR-BERT实现了最高的性能.
- 使用疾病类别与诊断代码相比,产生了类似的预测性能.
结论:
- 基于序列的NLP算法,特别是变压器模型,从疾病代码序列中为患者的结果提供了卓越的预测内容.
- 这些模型可以为各种临床预测生成多功能患者表示,即使不需要微调,也可以.
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