一个基于CHAT-23的机器学习模型,用于在中国儿童中早期查自闭症
Hengyang Lu1,2, Heng Zhang3, Yi Zhong1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
Frontiers in pediatrics
|September 25, 2024
概括
这项研究应用了机器学习来改进CHAT-23中国儿童自闭症查工具. 优化的模型使用更少的问题准确地识别出患有自闭症谱系障碍 (ASD) 风险的儿童.
科学领域:
- 神经发育障碍 神经发育障碍
- 儿科卫生保健 儿科卫生保健
- 医疗保健中的人工智能
背景情况:
- 自闭症谱系障碍 (ASD) 显著影响儿童的发展.
- 通过CHAT-23等问卷进行早期查对于及时干预至关重要.
- CHAT-23是中国广泛使用的23个问题选工具.
研究的目的:
- 为了提高中国儿童早期自闭症查的准确性.
- 使用机器学习优化CHAT-23问卷.
- 确定ASD风险评估中最相关的问题.
主要方法:
- 从中国无锡收集的临床数据.
- 使用最大相关性和最小冗余性 (mRMR) 进行特征选择.
- 开发和评估了七个监督机器学习模型.
主要成果:
- 最好的模型实现了0.909的灵敏度和0.922的特异性.
- 功能选择将问题数量减少到9个.
- 在识别有ASD风险的儿童方面表现出高精度.
结论:
- 机器学习为ASD早期查提供了更准确,更有效的方法.
- 改进CHAT-23问卷提高了诊断的相关性.
- 这项研究有助于改善中国儿童的健康状况.
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