解读孤独:可解释的人工智能可以帮助理解孤独的老年人的语言差异吗?
Ning Wang1, Sanchit Goel2, Stephanie Ibrahim3
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.
Psychiatry research
|July 14, 2024
概括
人工智能可以通过分析语音模式来检测老年人的孤独感. 这种方法识别了关键的语言特征,提供了一种新的方法来理解和潜在地解决孤独.
科学领域:
- 老年学是一门学科.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 孤独严重影响老年人的健康.
- 有效的,针对孤独的干预措施很少.
- 语音数据为评估孤独感的调查提供了一个个性化的替代方案.
研究的目的:
- 探索自然语言处理 (NLP) 和人工智能用于识别老年人孤独的语言标记.
- 用语音数据区分孤独和非孤独的个体.
- 在孤独检测中验证可解释AI (XAI) 的使用.
主要方法:
- 通过半结构面试收集了97名老年人的语音数据.
- 利用语言查询和词汇计数 (LIWC-22) 进行语言特征提取.
- 开发了一个XAI模型,根据社交关系和孤独等语音部分来分类孤独.
主要成果:
- 在检测孤独时,XAI模型实现了高精度 (0.889) 和回忆 (1.0).
- 关于社会关系和孤独的演讲部分是最有预测力的.
- 关键功能包括社会主题,代词使用和对话填充.
结论:
- 可解释的人工智能 (XAI) 能够有效地从非结构化语言中检测到孤独感.
- 语音分析提供了关于孤独生活经验的宝贵见解.
- 这种方法提供了一种新的方法来识别老年人群中的孤独感.
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