使用视觉语言模型的自动压力损伤评估和文档生成
Chao-Ying Chang1, Yu-Ting Shen1, Wen-Sheng Lien1
1National Yang Ming Chiao Tung University, Taipei, Taiwan.
Studies in health technology and informatics
|August 8, 2025
概括
一个人工智能模型自动化压力损伤评估和文档,提高准确性和效率. 这项技术提高了医疗保健机构的一致性,解决了标准化的关键需求.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 压力损伤评估和文档是至关重要的,但耗时的临床任务.
- 目前压力损伤评估的评价者间可靠性是不理想的,在60-70%之间.
研究的目的:
- 开发和评估压力损伤评估和临床描述生成的自动化方法.
- 利用佛罗伦萨-2视觉语言模型提高压力损伤评估的准确性和效率.
主要方法:
- 在946张压力损伤图像上训练佛罗伦萨-2模型,数据增强到每年级的275张图像.
- 使用该模型进行压力损伤的自动分级和标准化临床描述.
主要成果:
- 在压力伤害等级中取得了强的表现,F1得分从73.17%到95.00%.
- 在生成标准化临床描述方面表现出强大的能力,其BERTScore为85.58%.
结论:
- 自动化方法显示出在临床实践中提高评估一致性和文档效率的巨大潜力.
- 虽然在区分第三阶段和第四阶段的伤害方面仍然存在挑战,但该解决方案解决了压力伤害管理标准化和准确性的关键需求.
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