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Updated: Sep 9, 2025

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使用基于社交媒体足迹的双重深度Q网络框架诊断自闭症谱系障碍

Nesren S Farhah^1,2, Ahmed Abdullah Alqarni^2,3, Nadhem Ebrahim⁴

¹Department of Health Informatics, College of Health Science, Saudi Electronic University, Riyadh, Saudi Arabia.

Frontiers in medicine

|September 5, 2025

概括

这项研究使用Twitter数据和人工智能模型通过分析行为特征和情绪表达来识别自闭症谱系障碍 (ASD). 一个双深Q网络 (DDQN) 模型实现了87%的精度,显示了数字足迹症状分析的潜力.

科学领域:

神经科学
计算语言学
人工智能

背景情况:

社交媒体是医疗保健中的一个快速传播信息的工具.
自闭症谱系障碍 (ASD) 为数字参与带来了独特的挑战和机会.
推特是自闭症社区分享信息和经验的重要平台.

研究的目的:

使用机器学习分析Twitter数据以识别自闭症谱系障碍 (ASD).
检查自闭症患者在社交媒体上的行为特征和情绪表达.
通过在线交互来评估人工智能模型在发现自闭症方面的有效性.

主要方法:

使用推特作为分析自闭症相关内容的主要数据来源.
应用了先进的AI模型,包括CNN-LSTM,LSTM和DDQN-Inspired方法.
通过清理文本,令牌化和对二进制分类进行编码来预处理推特数据.

主要成果:

在DDQN-Inspired模型中,在识别自闭症方面取得了87%的高精度.
提出的方法显示出从社交媒体内容中识别自闭症的巨大潜力.
与现有系统进行比较显示了该框架的有效性.

结论:

开发的系统可以帮助临床医生研究数字足迹中的自闭症症状.
分析社交媒体文本变化为自闭症相关研究提供了一种新方法.
人工智能驱动的社交媒体分析有助于理解和支持自闭症患者.

关键词:

人工智能自闭症谱系障碍深度学习进行诊断其他残疾社交媒体

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