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使用电子健康记录的变压器患者嵌入使患者分层和进展分析成为可能

Su Xian¹, Monika E Grabowska², Iftikhar J Kullo³

¹Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA. suxian06@gmail.com.

NPJ digital medicine

|August 14, 2025

概括

这项研究引入了一种无监督的方法来分析电子健康记录 (EHR),揭示新的疾病模式和患者异质性. 该方法准确地预测未来的疾病,并揭示了各种健康结果,改进了医疗数据分析.

科学领域:

计算生物学计算生物学
医疗信息学医疗信息学
数据科学数据科学数据科学

背景情况:

电子健康记录 (EHR) 的二次使用通常依赖于领域专业知识.
在EHR数据中发现新的医疗模式需要先进的表示方法.
嵌入患者级数据可以描述疾病异质性并识别结果差异.

研究的目的:

引入一种无监督的方法,在患者层面嵌入高维电子病历数据.
描述疾病异质性,并确定与临床结果差异相关的新模式.
为了利用患者的嵌入来改善疾病预测和表型.

主要方法:

应用了一个无监督嵌入方法从eMERGE网络的纵向患者事件.
利用了34,851个独特的医疗代码和超过100万个患者事件.
评估模型在预测未来疾病和批量表型化方面的性能.

主要成果:

取得了强大的预测性能:未来疾病预测 (1年) 的中位数AUROC为0.87,批量表型化为0.84.
患者嵌入揭示了不同的并发症概况和健康结果.
在结直肠癌和全身性红斑狼中确定了不同的亚型和进展模式.

结论:

无监督的患者级EHR嵌入是发现复杂疾病模式的强大工具.
这种方法可以描述患者的异质性,并确定临床结果的差异.
这种方法表明,在推进精准医学和临床研究方面有很大的潜力.

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