公共卫生 公共卫生
Qiuran Rita Lyu1, Yanping Li2, Kevin Shen3
1University of California, Berkeley, Berkeley, CA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
这项研究介绍了ADLLM,一种先进的语言模型,可以使用多式数据准确预测阿尔茨海默病 (AD) 生物标志物和认知衰退. ADLLM对早期AD诊断和监测AD进展具有前途.
科学领域:
- 人工智能在医学中的应用
- 计算神经科学是一种神经科学.
- 生物标志物发现发现
背景情况:
- 预测阿尔茨海默病 (AD) 的进展和个体生物标志物状态对于患者护理至关重要.
- 高级语言模型 (LLM) 可以整合各种数据类型,以揭示AD中的复杂关系.
研究的目的:
- 开发和验证ADLLM,这是一个增强的LLM,用于预测AD相关的结果.
- 评估模型处理多式联运数据的能力,并提供可解释的预测.
主要方法:
- 训练了ADLLM,一个基于LLaMA 3.1的LLM,使用LoRA进行微调,基于ADNI,HABS-HD和POINTER队伍的数据.
- 利用人口统计,神经成像,液体生物标志物和临床评估作为Aβ/tau负担和MMSE变化的预测指标.
- 采用了用于多式联接输入处理的文本提示,并使用AUC,准确性和特异性评估性能.
主要成果:
- ADLLM在各队伍中取得了强大的预测性能,AUC对Aβ负担高达0.825和tau负担高达0.730.
- 该模型准确地预测了MMSE变化率,AUC为0.806 (ADNI) 和0.816 (HABS-HD).
- 注意力分析揭示了队列特定的变量重要性,突出了CSF生物标志物的ADNI和HABS-HD等离子体生物标志物的作用.
结论:
- ADLLM有效地预测使用多模式输入的复合AD结果,并提供可解释的变量重要性.
- 该模型在各个队列和任务中显示出一致的性能,表明其作为AD诊断和监测的可扩展临床工具的潜力.
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