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相关概念视频

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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相关实验视频

Updated: Sep 12, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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机器学习中的算法公平和自闭症的预测使用电子健康记录.

Amber M Angell1, Yongqiu Li2, Jiang Bian2

  • 1University of Southern California, Los Angeles, CA, USA.

Studies in health technology and informatics
|August 8, 2025
PubMed
概括

使用电子健康记录 (EHR) 进行自闭症谱系障碍 (ASD) 诊断的机器学习模型显示出重大公平性问题. 这些模型在性别上表现不公平,突出了自闭症识别中的偏见.

关键词:
自闭症 自闭症 自闭症电子健康记录是电子健康记录.预测建模预测建模

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算精神病学是一种计算精神病学.
  • 医疗保健中的机器学习

背景情况:

  • 早期诊断自闭症谱系障碍 (ASD) 对干预至关重要.
  • 应用于电子健康记录 (EHR) 的机器学习 (ML) 对自闭症识别有希望.
  • 现有的ASD诊断工具表现出基于性别的差异,需要公平的ML模型.

研究的目的:

  • 使用EHR数据开发基于ML的ASD诊断预测模型.
  • 评估这些ML模型跨性别的算法公平性.
  • 识别和量化男孩和女孩之间的自闭症预测中的潜在偏差.

主要方法:

  • 追溯病例控制研究设计.
  • 从EHR中利用了大量的ASD和无ASD儿童队伍 (70,803例ASD病例,212,409例对照).
  • 开发了后勤回归和XGBoost模型,通过准确性,回忆,精度,F1得分和AUC等指标来评估性能;使用机会平等和均赔率来评估公平性.

主要成果:

  • 使用EHR数据进行ASD预测的ML模型展示了重要的公平性问题.
  • 在男孩和女孩之间观察到绩效差异,表明潜在的偏见.
  • 标准性能指标没有完全捕捉算法偏差的程度.

结论:

  • 目前使用EHR诊断ASD的ML模型对性别不公平.
  • 算法公平性评估对于开发可靠的ASD预测工具至关重要.
  • 需要进一步的研究来缓解偏见,并确保所有儿童均可识别ASD.