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相关概念视频

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使用机器学习模型预测产前抑郁症和评估模型偏差.

Yongchao Huang1, Suzanne Alvernaz1, Sage J Kim2

  • 1Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, IL, USA.

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PubMed
概括
此摘要是机器生成的。

机器学习模型可以使用电子健康记录在怀孕早期适度预测产周抑郁症. 然而,这些模型显示对低收入少数民族妇女的偏见,对这些人群的表现不太准确.

关键词:
围产期抑郁症 围产期抑郁症电子医疗记录 电子医疗记录机器学习是机器学习.模型性能偏差是指模型的性能偏差.

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科学领域:

  • 生殖健康 生殖健康
  • 计算医学是一种计算医学.
  • 健康差异 在健康上的差异

背景情况:

  • 围产期抑郁症 (PND) 影响10-20%的孕妇,在黑人和拉丁裔妇女中患病率更高,她们在诊断和治疗方面面临差异.
  • 现有的机器学习 (ML) 模型用于PND预测,通常是根据多数人口的数据进行训练,当应用于种族和民族少数群体时,会表现出偏见和表现不佳.
  • 这项研究解决了评估ML模型在预测怀孕早期抑郁症的有效性和公平性的需要,特别是在主要是低收入的少数患者群体中.

研究的目的:

  • 用EMR数据评估ML模型在预测种族/少数民族妇女早期怀孕抑郁症方面的有效性.
  • 在这个人口群体中确定与PND相关的因素.
  • 评估少数群体的ML模型表现中的潜在偏差.

主要方法:

  • 利用了城市医院的5875名患者的EMR数据,为低收入的黑人和西班牙裔妇女提供服务.
  • 使用患者健康问卷-9 (PHQ-9) 进行抑郁症状评估.
  • 调查了多个ML分类器,包括弹性网,并使用SHAP进行解释和偏差评估 (差异影响,平等机会差异).

主要成果:

  • 弹性净ML模型实现了0.67.7的低ROCAUC.
  • 确定已知的PND预测因子 (例如,意外怀孕,单身状态) 和新的因素 (例如,自我报告的疼痛,低产前维生素摄入量,喘,男性胎儿,低血小板计数).
  • 对于占主导地位的低收入少数群体样本 (75%的少数群体,54%的黑人,27%的拉丁裔) 的模型表现明显较低.

结论:

  • 机器学习模型显示了使用EMR数据在怀孕早期适度预测围产期抑郁症的潜力.
  • 存在显著的绩效差异,ML模型表明对低收入和少数民族妇女的固有偏见.
  • 进一步的研究对于为不同的孕产妇健康群体开发公平而准确的ML工具至关重要.