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基于自动编码器的代表性学习类似患者从电子健康记录中检索:比较研究

Deyi Li¹, Aditi Shukla², Sravani Chandaka³

¹Department of Health Outcomes & Biomedical Informatics, University of Florida, 1889 Museum Rd, 7th Floor, Suite 7000, Room 7012, Gainesville, FL, 32611, United States, 1 352-627-9143.

JMIR medical informatics

|July 24, 2025

概括

排斥自编码器擅长使用欧几里德距离找到类似的患者,优于其他模型. 学习率和远程测量对个性化医学患者代表性学习的表现产生重大影响.

科学领域:

医疗信息学医学信息学
机器学习在医疗保健中的应用
计算生物学是一种计算生物学.

背景情况:

电子健康记录 (EHR) 数据建模具有挑战性,因为其高维度,混合特征,噪音,偏差和稀疏性.
使用自动编码器 (AEs) 的患者代表学习提供了一种有前途的方法来解决这些EHR数据挑战.
了解不同的AE设计和距离测量对获取类似患者队伍的影响至关重要.

研究的目的:

评估五种常见的自编码器 (AE) 变体在检索类似患者中的性能.
调查各种距离测量和超参数配置对AE模型性能的影响.
评估基于AE的患者相似性估计对临床结果预测的有效性.

主要方法:

在两个真实世界EHR数据集上测试了五种AE变体 (香草,无噪声,收缩,稀疏,坚固).
应用k-最近邻 (k-NN) 与欧几里德和马哈拉诺比斯距离到AE产生的潜伏表示.
对预测急性损伤发作和出院后1年死亡率的评估模型性能.

主要成果:

当与欧几里德距离 (P<.001) 配对时,Denoising自编码器显著超过了其他AE变体.
学习率被确定为影响AE模型性能的关键超参数.
马哈拉诺比斯基于距离的k-NN在隐性表示上经常胜过基于欧几里德距离的k-NN,尽管直接应用到原始数据是数据依赖的.

结论:

这项研究为患者相似性检索提供了AE变体的全面分析.
研究结果强调了AE设计和超参数调对于有效的患者代表性学习的重要性.
结果为开发基于AE的先进方法奠定了基础,以实现个性化医疗和改善患者护理.

关键词:

为卫生专业人员提供决策支持.电子健康记录是电子健康记录.机器学习是机器学习.医疗信息学的方法和工具.

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