开发一种简化措施来预测自闭症谱系障碍的风险:在中国使用机器学习方法缩写M-CHAT-R
Ning Pan1, Lifeng Chen1, Bocheng Wu1
1Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510630, China.
Psychiatry research
|January 10, 2025
概括
机器学习有效地选了幼儿的自闭症谱系障碍 (ASD),使用了幼儿自闭症减少修改的检查清单,修订了 (M-CHAT-R) 项目集,改善了初级保健的早期检测.
科学领域:
- 发育儿科 发育儿科
- 计算精神病学是一种计算精神病学.
- 公共卫生查 公共卫生查
背景情况:
- 早期识别自闭症谱系障碍 (ASD) 对及时干预至关重要.
- 中国初级儿童保育机构目前的查工具在准确性和效率方面存在局限性.
研究的目的:
- 开发一种机器学习模型,以改善幼儿的ASD查.
- 从M-CHAT-R中确定关键指标和社会人口统计因素,以区分自闭症与典型发展.
主要方法:
- 利用了中国M-CHAT-R (n=6,049名幼儿) 验证研究的数据.
- 整合了17个社会人口和环境风险因素与M-CHAT-R数据.
- 应用了五种特征选择方法和六种机器学习算法来识别最佳选子集.
主要成果:
- 确定了三个特征子集,最佳分类器使用了子集2中的七个项目.
- 性能最高的模型实现了92.5%的灵敏度和90.1%的特异性.
- 七项分类器包括与社交互动和注意力相关的M-CHAT-R项目,以及孩子的年龄.
结论:
- 机器学习模型可以有效地区分患有自闭症的幼儿和通常发育的幼儿,使用紧缩的M-CHAT-R项目集.
- 优化机器学习模型对于在初级医疗保健及其他领域的ASD查具有显著的临床价值.
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