用大语言和视觉语言模型检测阿尔茨海默病的新思维链推理方法
概括
本研究介绍了一种使用大型语言模型 (LLM) 为老年人护理的隐私友好型设备上代理. 它使用多式联络数据和思维链推理将阿尔茨海默病 (AD) 的分类提高了16.7%.
科学领域:
- 老年学和人工智能的人工智能
- 计算语言学和计算机视觉
背景情况:
- 超级老年社会面临着痴呆症患病率的增加,需要先进的护理解决方案.
- 现有的痴呆症诊断模型往往缺乏全面的支持和隐私功能.
- 在设备上的处理对于实时,安全的老年护理应用程序至关重要.
研究的目的:
- 开发一种基于设备上大型语言模型 (LLM) 的多式联络代理,用于全面的老年护理.
- 通过使用保护隐私的人工智能增强认知评估和阿尔茨海默病 (AD) 分类.
- 实现对老年人的实时,综合支持,而无需依赖云计算.
主要方法:
- 利用自动语音识别 (ASR) 和视觉语言模型 (VLMs) 来进行多式联网数据输入.
- 在一个紧的LLM的监督微调 (SFT) 期间使用的思维链 (CoT) 推理.
- 整合了一个线性层,用于对阿尔茨海默病 (AD) 与非AD病例的二元分类.
主要成果:
- 在使用CoT推理的AD分类中实现了16.7%的相对性能改善.
- 证明了多模式数据 (语音和图像) 对认知评估的有效性.
- 验证了在设备上处理隐私保护老年护理的可行性.
结论:
- 拟议的多式联络,设备上的LLM代理提供了一种全方位老年护理的新方法.
- 思想链推理显著提高了痴呆症分类的准确性.
- 这一战略为超级老年社会中先进的,保护隐私的解决方案铺平了道路.
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