无监督机器学习方法用于间接估计波多黎各人口中慢性病的参考间隔
Julian Velev1,2, Jack LeBien3, Abiel Roche-Lima4
1Department of Physics, University of Puerto Rico, San Juan, PR, 00925-2537, USA. julian.velev@upr.edu.
Scientific reports
|October 11, 2023
概括
这项研究使用机器学习来创建实验室测试的个性化参考间隔,改善不同人群的诊断. 它揭示了实验室值如何因年龄和性别而异,有助于早期发现疾病.
科学领域:
- 临床化学 临床化学
- 医疗保健中的机器学习
- 人口健康 人口健康
背景情况:
- 参考间隔 (RIs) 对于诊断和治疗患者至关重要,但其建立往往是昂贵和耗时的.
- 当前的RI常常未经验证,并被普遍应用,忽视了显著的人口差异 (性别,年龄,种族).
- 现有的异常标志是基本的,仅仅表示一个值是否超出已确定的RI.
研究的目的:
- 开发无监督的机器学习方法,以确定准确的,特定于人群的参考间隔.
- 利用大规模的常规临床实验室数据来完善诊断工具.
- 调查性别和年龄对波多黎各人口实验室值的影响.
主要方法:
- 利用无监督机器学习,特别是高斯混合模型,分析数百万例行实验室结果.
- 专注于波多黎各人口中慢性病相关的分析物.
- 检查了多个实验室措施的联合分布,以提高诊断能力.
主要成果:
- 建立了关键分析物的性别和年龄依赖的参考间隔.
- 确定了与年龄相关的正常器官功能下降和衰竭的证据.
- 证明了分析实验室测量的联合分布可以显著提高诊断价值.
结论:
- 无监督机器学习可以有效地从例行实验室数据中推导出精确的,人口特异性的参考间隔.
- 慢性病标志物的实验室值显然受研究人口的年龄和性别的影响.
- 整合实验室结果的联合分布分析可以提高诊断准确性和临床实用性,特别是在健康差异人口中.
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