来自诊断代码的患者嵌入 医疗保健预测任务:Pat2Vec机器学习框架
Edgar Steiger1, Lars Eric Kroll1
1Zi Data Science Lab, Department IT and Data Science, Central Research Institute of Ambulatory Health Care in Germany (Zi), Berlin, Germany.
JMIR AI
|June 14, 2024
概括
Pat2Vec是一个新的机器学习框架,从健康记录中创建数值患者诊断档案. 这种方法改善了数据分析,以获得更好的医疗保健结果和资源规划.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 计算医学是一种计算医学.
背景情况:
- 医疗保健索赔和电子健康记录中的诊断代码对于数据驱动的决策至关重要.
- 像二进制编码这样的现有方法与高可变性,数据缺口和大量诊断作斗争.
- 对于医疗保健中的机器学习应用来说,对患者诊断资料的强大的数值表示是必不可少的.
研究的目的:
- 引入Pat2Vec,一种自我监督的机器学习框架,用于将完整的患者诊断资料嵌入到数值向量中.
- 利用自然语言处理启发的技术来创建紧的,实值的患者健康数据表示.
主要方法:
- 在德国门诊病理索赔数据 (ICD-10代码) 上使用贝叶斯优化开发了一个最佳的矢量化嵌入模型.
- 在多个机器学习算法中使用多种回归和分类任务对模型进行校准.
- 根据使用超过1000万个患者记录 (2016-2019) 的基线二进制编码模型进行验证,包括患者载体的集群和2D可视化.
主要成果:
- Pat2Vec模型在相同的维度中超过了基线二进制编码模型,证明了对缺失数据的优越稳定性.
- 观察到显著的性能增长,特别是在较低的维度,表明有效压缩非线性信息.
- 该框架可扩展,以集成额外的医疗保健数据源,并可应用于现有的诊断数据集.
结论:
- Pat2Vec提供了一种强大的工具,可以通过个性化的预防和患者监控信号检测来提高医疗保健质量.
- 数据驱动的机器学习框架通过识别不同的患者分队来促进有效的医疗保健资源规划.
- 公开共享的嵌入模型使得分析患者诊断数据的应用和研究更广泛.
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