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
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.
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.
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