这是新生儿感到疼痛吗? 利用临床知识进行基于大型语言模型的高精度新生儿疼痛评估
Lucas Pereira Carlini1, Leonardo Antunes Ferreira2, Gabriel de Almeida Sá Coutrin2
1Department of Electrical Engineering, University College FEI, Av. Humberto de Alencar Castelo Branco, 3972-B, São Bernardo do Campo, São Paulo, Brazil. lucaspcarlini10@gmail.com.
Pediatric research
|December 11, 2025
概括
本研究介绍了用于新生儿疼痛评估的视觉语言模型,为主观尺度提供了客观的替代方案. 人工智能实现了高精度,特别是在分析面部特征时,改善了重症监护中的婴儿的疼痛评估.
科学领域:
- 新生儿重症监护室新生儿重症监护室
- 医学中的人工智能
- 疼痛评估 疼痛评估 疼痛评估
背景情况:
- 在NICU的新生儿经常经历痛苦的手术.
- 没有治疗的疼痛会导致长期的认知和行为问题.
- 目前的疼痛评估是主观和不一致的.
研究的目的:
- 利用人工智能开发一种客观的新生儿疼痛评估方法.
- 评估视觉语言模型 (VLMs) 对此任务的性能.
- 探索用于优化VLM性能的提示工程策略.
主要方法:
- 使用视觉语言模型 (VLM) 进行自动疼痛评估 (APA).
- 开发了新的提示类别:利用潜在的临床知识并提供面部特征信息.
- 基于不同提示策略的性能比较.
主要成果:
- 利用隐藏的知识产生了82.3%的精度和73.2%的回忆.
- 评估面部特征实现了100%的精度和40.1%的回忆.
- 这种VLM方法的表现优于以前的深度学习方法.
结论:
- VLMs为新生儿疼痛评估提供了一种新的,高精度的方法.
- 快速设计对于有效利用VLM功能至关重要.
- 这种由人工智能驱动的方法对现实世界的临床应用非常有希望.
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