评估幼儿自闭症特征使用数据驱动方法与DSM-5绘图
Neda Abdelhamid1, Rajdeep Thind2, Heba Mohammad3
1Abu Dhabi School of Management, Abu Dhabi P.O. Box 6844, United Arab Emirates.
Bioengineering (Basel, Switzerland)
|October 28, 2023
概括
在幼儿中早期发现自闭症谱系障碍 (ASD) 是至关重要的. 机器学习模型有效地识别了与沟通,社交互动和重复行为相关的关键行为特征,以便及时诊断ASD.
科学领域:
- 神经发育障碍 神经发育障碍
- 儿科心理学 儿科心理学
- 机器学习在医疗保健中的应用
背景情况:
- 自闭症谱系障碍 (ASD) 是一种影响沟通,社会互动和行为的神经发育状况.
- 早期发现幼儿的自闭症特征对于及时干预和获得医疗保健至关重要.
- 通过识别特定的早期行为指标,可以增强现有的诊断方法.
研究的目的:
- 为了确定早期的行为特征,表明ASD在幼儿.
- 将已识别的特征与精神障碍诊断和统计手册 (DSM-5) 神经发育标准进行映射.
- 提出一种基于机器学习的数据流程,用于在幼儿时期检测ASD.
主要方法:
- 研究了幼儿的各种自闭症行为特征.
- 使用特征选择技术来识别相关的行为指标.
- 开发了分类模型,包括贝叶斯网络 (Bayes Net) 和物流回归 (LR),使用选定的特征.
- 将已识别的特征映射到DSM-5神经发育区域.
主要成果:
- 与沟通,社交互动和重复行为相关的认知特征对幼儿ASD查最相关.
- 机器学习模型 (贝叶斯网络,LR) 使用ASD行为数据子集展示了一致的预测准确性.
- 这项研究成功地确定了用于早期发现自闭症的关键行为标志物.
结论:
- 机器学习技术适用于预测幼儿的自闭症谱系障碍 (ASD).
- 早期识别特定的行为特征可以显著帮助诊断和管理ASD.
- 拟议的数据处理为早期发现自闭症提供了一个有前途的方法,促进了迅速的临床转诊和医疗保健的获取.
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