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Electrophysiological Measurements and Analysis of Nociception in Human Infants
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在婴儿哭声中进行疼痛分类的视觉语言模型.

Anthony McCofie1, Abhiram Kandiyana1, Peter R Mouton2

  • 1Computer Science and Engineering, University of South Florida, Tampa, Florida, USA.

Proceedings. IEEE International Symposium on Computer-Based Medical Systems
|September 22, 2025
PubMed
概括
此摘要是机器生成的。

检测婴儿疼痛是一个挑战. 这项研究使用GPT-4(V) 和Mel光谱图以最小的数据准确检测婴儿疼痛,提高可解释性.

关键词:
几次射击提示提示的提示婴儿疼痛检测 婴儿疼痛检测大型语言模型疼痛的分类,疼痛的分类视觉语言模型 视觉语言模型

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科学领域:

  • 人工智能的人工智能
  • 婴儿健康 婴儿健康
  • 信号处理 信号处理

背景情况:

  • 准确的婴儿疼痛检测是至关重要的,但具有挑战性.
  • 传统的深度神经网络需要大量的数据集和计算能力,缺乏可解释性.
  • 现有的方法在数据稀缺和透明度方面扎.

研究的目的:

  • 为了引入一种新的,可解释的方法,用于婴儿疼痛检测,使用少数射击学习.
  • 为了利用视觉语言模型 (GPT-4(V)) 与乳腺谱图进行增强的婴儿哭声分析.
  • 减少对婴儿疼痛分类中广泛标记数据集的依赖.

主要方法:

  • 使用了OpenAI的GPT-4 (V) 视觉语言模型.
  • 采用了婴儿哭声的Mel谱图表示.
  • 实施了几次射击提示策略来进行分类.
  • 在USF-MNPAD-II数据集上验证了方法.

主要成果:

  • 在婴儿疼痛检测中达到83.33%的准确性.
  • 仅需要16个培训样本,与基线的4914个样本相比大幅减少.
  • 与传统方法相比,证明了提高透明度和可解释性.

结论:

  • 用视觉语言模型进行少数拍摄提示,为婴儿疼痛检测提供了一个有希望的解决方案.
  • 这种方法大大减少了数据和计算需求.
  • 代表了GPT-4o用于婴儿疼痛分类的新应用.