创建自闭症谱系障碍的诊断评估模型,通过通过机器学习来区分词法选择
Sumi Kato1,2, Kazuaki Hanawa3, Manabu Saito4
1Department of Neuropsychiatry, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.
PloS one
|September 27, 2024
概括
对口语的词典图分析有助于区分自闭症谱 (AS) 和非AS情况. 使用语言标签和文本分析的机器学习模型在青少年和成人中诊断AS时达到80%的准确性.
科学领域:
- 语言学的语言学.
- 心理学 心理学 心理学
- 计算机科学 计算机科学
背景情况:
- 在青少年和成年人中,区分自闭症谱 (AS) 和非AS疾病存在挑战,原因是AS异质性和诊断工具的局限性.
- 像ADOS-2这样的传统诊断工具在捕捉AS呈现的全谱方面存在局限性.
- 需要采用多维诊断方法来提高准确性.
研究的目的:
- 探索词典图形分析在区分AS和非AS条件的有用性,在青少年和成年人.
- 开发和评估基于机器学习的诊断模型,利用语言特征.
- 为了比较面试和讲故事的口语任务的诊断效果.
主要方法:
- 收集了来自64名AS患者和71名非AS患者 (14岁以上) 的口语数据 (采访,讲述故事).
- 应用机器学习技术来分析收集的文本中的词法选择.
- 开发了两种诊断模型:一种基于语言标签,另一种将标签与文本分析结合起来.
主要成果:
- 组合模型实现了高诊断效率:80%的准确性,82%的精度,73%的灵敏度和87%的特异性.
- 基于采访的文本在诊断上被证明比讲故事的文本更有效.
- 词典图形分析确定了AS个体的独特语言模式,反映了改变的社会语言使用.
结论:
- 词典图形分析是对青少年和成年人AS诊断传统方法的一个有希望的补充.
- 自然语言处理可以检测语言模式,以提高AS的诊断准确性.
- 改变社会语言的使用是AS和非AS条件之间的关键差异化因素.
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