使用预训练的视觉转换器和大型语言模型来预测发作
Paras Parani1, Umair Mohammad1, Fahad Saeed1
1Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA.
概括
大型语言模型 (LLM) 在预测患者的发作方面表现有前途. 这种人工智能方法实现了比视觉变压器更高的准确性,可能改善患者的生活质量.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 预测发作对于患者的福祉至关重要.
- 预测的挑战包括数据的变化和注释的复杂性.
- 现有的模型需要专门的知识和广泛的数据.
研究的目的:
- 探索预先训练有素的人工智能模型对预测的有效性.
- 为了比较大型语言模型 (LLM) 和视觉转换器 (ViT) 在预测中的性能.
- 解决研究中传统的监督学习模型的局限性.
主要方法:
- 使用预先训练的视觉转换器 (ViTs) 和大型语言模型 (LLMs).
- 输入,嵌入和分类层的最小化精细化.
- 采用患者独立的预测方法.
主要成果:
- 在患者独立的发作预测方面,LLM的表现优于ViT.
- LLM的灵敏度达到了79.02%,比ViTs.8%高.
- 与基于ResNet的定制模型相比,LLM显示了12%的改进.
结论:
- 预先训练有素的LLM在预测发作方面是可行的和有效的.
- 这种方法为患者的生活质量提供了潜在的改善.
- 开源代码可用于进一步的研究和开发.
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