在数据稀缺的情况下快速创伤分类:一种结合自然语言处理和机器学习的紧急现场决策模型.
Jun Tang1, Tao Li2, Liangming Liu3
1Department of Information, Daping Hospital, Army Medical University, Chongqing, 400042, China.
Medical & biological engineering & computing
|July 11, 2025
概括
本研究介绍了一种使用自然语言处理 (NLP) 和机器学习 (ML) 的AI模型,用于在紧急情况下快速分类创伤伤害. 人工智能模型显著提高了预测准确性,为更快,更有效的紧急医疗治疗提供了更好的预测准确性.
科学领域:
- 紧急医疗 紧急医疗
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 创伤是全球发病率和死亡率的主要原因之一.
- 有效的应急响应需要快速的伤害分类来分配资源和优先考虑治疗.
- 混乱的紧急场景阻碍了及时和准确的数据收集.
研究的目的:
- 在有限的数据条件下,为创伤患者开发一个快速,分层的医疗治疗模型.
- 整合自然语言处理 (NLP) 和机器学习 (ML) 以改善紧急救援行动.
- 在危急情况下提高伤害分类的准确性和效率.
主要方法:
- 利用了重庆大平医院 (2013-2024) 的26810名创伤患者的数据集.
- 开发了一个双层模型,将NLP用于非结构化文本和四个ML算法用于结构化数据.
- 对来自重庆急诊中心的245例病例进行了外部验证.
主要成果:
- 拟议的NLP和ML模型在测试数据集上实现了91.17%的准确性,超过了MLP模型的4.33%.
- 实现了高性能指标:97.06%的特异性,86.85%的F1得分和0.949的AUC.
- 外部验证证明了强大的概括性,准确度为87.35%,特异性为95.78%,F1评分为80.37%,AUC为0.848.
结论:
- 集成的NLP和ML模型能够使用有限的紧急数据快速分层医疗治疗.
- 该模型在预测准确性和紧急创伤护理的概括性方面具有显著的优势.
- 人工智能驱动的方法可以改变紧急救援模式,提高效率和患者的治疗结果.
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