深度ASD:一种深度对抗性规范化的图形学习方法,用于ASD诊断,使用多式联络数据
Wanyi Chen1,2, Jianjun Yang3, Zhongquan Sun1
1Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Translational psychiatry
|September 14, 2024
概括
DeepASD是一种新的图形学习方法,通过整合多式联络数据和患者关系来改善自闭症谱系障碍 (ASD) 诊断. 这种方法提高了对ASD病原学的理解,并提高了诊断的准确性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 自闭症谱系障碍 (ASD) 在诊断和理解其潜在机制方面存在复杂的挑战.
- 自闭症并发症对心理健康有重大影响,需要精确的诊断工具.
- 单一模式数据往往无法捕捉到ASD的全部复杂性.
研究的目的:
- 利用多式联络数据开发一种先进的自闭症谱系障碍 (ASD) 预测方法.
- 通过结合患者之间的关系来增强对ASD病原学的理解.
- 提高ASD诊断的准确性和全面性.
主要方法:
- 提出了DeepASD,一个端到端可训练的规范化图形学习框架.
- 综合异构的多式联络数据和潜在的患者间关系.
- 采用多式对抗规则化编码器用于特征表示和图形神经网络进行分类.
主要成果:
- 与ABIDE数据集中的八种最先进的方法相比,DeepASD实现了更高的性能.
- 在准确度 (13.25%),AUC-ROC (7.69%) 和特异性 (17.10%) 中显著改善.
- 有效地利用多式联络数据和患者关系,提高ASD预测.
结论:
- 深度ASD提供了一个有前途的方法,可以更全面地了解ASD机制.
- 该方法有可能显著改善ASD诊断性能.
- 整合多式联络数据和患者关系是推动自闭症研究的关键.
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