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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, Illinois.

Biological psychiatry global open science
|October 14, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测怀孕早期的围产期抑郁症,但对少数民族妇女有偏见. 需要进一步的研究来提高准确性和减少这些关键健康预测的差异.

关键词:
电子医疗记录电子医疗记录在健康方面存在差异.机器学习 机器学习模型性能偏差是指模型的性能偏差.围产期抑郁症 围产期抑郁症

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科学领域:

  • 生殖健康 生殖健康
  • 医疗信息学 医疗信息学
  • 健康差异 在健康上的差异

背景情况:

  • 围产期抑郁症影响10-20%的孕妇,在黑人和拉丁裔妇女中患病率更高,诊断率更低.
  • 现有的产后抑郁症机器学习 (ML) 模型往往缺乏多样性,导致有偏见的预测.
  • 本研究探讨了在预测围产期抑郁症的ML模型中种族/族裔少数群体的代表性不足.

研究的目的:

  • 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.
  • 利用电子病历 (EMR) 数据在不同人群中预测抑郁风险.
  • 在不同种族/种族群体的ML模型表现中识别潜在的偏差.

主要方法:

  • 利用了来自美国城市医院的5875名妇女的EMR数据,为低收入黑人和西班牙裔人口提供服务.
  • 使用患者健康问卷-9 (PHQ-9) 评估抑郁症状严重程度.
  • 采用多个ML分类器和沙普利添加式解释来解释和偏差评估,使用四个指标.

主要成果:

  • 最好的ML模型 (弹性网) 实现了较低的预测性能 (AUC = 0.61).
  • 确定已知的风险因素 (意外怀孕,单身状态) 和新的因素 (疼痛,低维生素摄入量,喘,男性胎儿,低血小板).
  • 黑人 (AUC=57%) 和拉丁裔 (AUC=59%) 女性的模型表现较差,而白人女性 (AUC=64%) 的表现较差,尽管样本的重点是少数群体.

结论:

  • 基于EMR的ML模型为预测怀孕早期抑郁症提供了适度的潜力.
  • 对低收入少数民族妇女观察到显著的绩效偏差.
  • 解决数据多样性和模型公平性对于公平的围产期精神卫生保健至关重要.