儿童自闭症检测:将机器学习和自然语言处理集成到叙事分析中
Charalambos K Themistocleous1, Maria Andreou2, Eleni Peristeri3
1Department of Special Needs Education, Faculty of Educational Sciences, University of Oslo, 0313 Oslo, Norway.
Behavioral sciences (Basel, Switzerland)
|June 26, 2024
概括
人工智能 (AI) 模型现在可以使用语言分析准确地识别儿童的自闭症谱系障碍 (ASD). 这项技术为早期诊断提供了更快,更客观的工具,改善了获得关键早期干预服务的机会.
科学领域:
- 计算语言学计算语言学
- 发展心理学是发展心理学.
- 机器学习是机器学习.
背景情况:
- 早期识别自闭症谱系障碍 (ASD) 对改善结果至关重要.
- 希腊的诊断平均为6年,低收入家庭或少数民族家庭的诊断时间进一步延迟.
- 目前对自闭症的语言评估是耗时和劳动密集的,阻碍了早期诊断.
研究的目的:
- 开发一种可靠且实用的人工智能 (AI) 模型,用于早期识别自闭症.
- 使用叙事和词汇技能,区分患有自闭症儿童与典型发育的同龄人.
- 创建客观的工具,增加获得早期ASD评估和诊断的机会.
主要方法:
- 应用自然语言处理 (NLP) 来从讲故事中提取语言特征.
- 利用机器学习模型,对68名自闭症儿童和52名典型发育儿童的叙事和词汇数据进行训练.
- 决策树,梯度提升,历史梯度提升和XGBoost模型的性能比较.
主要成果:
- 人工智能模型在区分患有自闭症儿童和典型发育的同龄人方面实现了96%的准确性.
- 历史梯度增强和XGBoost模型在准确性和F1得分方面表现出卓越的性能.
- 通过NLP提取的语言特征有效地区分了这两个群体.
结论:
- 人工智能驱动的语言分析提供了一种严格而客观的方法来识别ASD.
- 这项技术可以显著提高早期诊断的可访问性,特别是对于服务不足的人群.
- 机器学习的部署有望提高ASD儿童的早期识别服务.
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