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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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相关实验视频

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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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自闭症频谱诊断的预测模型,来自使用机器学习的婴儿电子数据.

Ayelet Ben-Sasson1, Joshua Guedalia1, Liat Nativ1

  • 1Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa 3498838, Israel.

Children (Basel, Switzerland)
|April 27, 2024
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概括

机器学习模型可以使用电子健康记录预测婴儿的自闭症谱系障碍 (ASD). 通过这种方法的早期检测可以识别高风险的婴儿,以便及时进行干预.

关键词:
自闭症谱系障碍 自闭症谱系障碍发展发展发展发展发展.电子健康记录是电子健康记录.机器学习是机器学习.查检查 查检查 查检查 查检查

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科学领域:

  • 儿科 儿科 儿科
  • 发育神经科学的发展神经科学.
  • 医疗保健中的机器学习

背景情况:

  • 早期发现自闭症谱系障碍 (ASD) 对有效干预至关重要.
  • 目前的诊断时间表往往超过三岁,延迟了关键的支持.
  • 使用常规收集的健康数据进行预测建模提供了一个潜在的解决方案.

研究的目的:

  • 开发和评估用于预测婴儿ASD诊断的机器学习模型.
  • 使用来自国家查计划的电子健康记录 (EHR) 来进行预测.
  • 确定用于早期ASD识别的关键预测因素.

主要方法:

  • 对780,610名儿童的电子病历进行了回顾性队列研究,其中有1163名患有自闭症.
  • 梯度增强模型使用100个参数进行3倍交叉验证.
  • 沙普利添加剂解释工具用于特征重要性量化.

主要成果:

  • 该模型实现了ROC曲线下的平均面积为0.86 (SD < 0.002).
  • 确定了一个高风险群体,ASD发病率高4.3倍.
  • 关键预测因素包括发育里程碑延迟 (语言,社会,运动),男性性别,父母的担忧,以及出生/成长因素.

结论:

  • 机器学习模型可以有效地使用预防性护理的EHR数据预测ASD.
  • 这种方法通过分析各种因素的复杂相互作用来促进早期ASD查.
  • 将其纳入常规健康检查中可以改善ASD的及时识别和干预.