通过大型语言模型从患者笔记中快速,准确地分配临床诊断:作为一个用例的临床肺炎:临界儿童肺炎
Blake Martin1,2, Marisa Payan3, Jaime LaVelle4
1Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO.
Critical care explorations
|November 28, 2025
概括
一个定制的生成预训练变压器 (GPT) -4o大语言模型 (LLM) 准确地识别了使用临床笔记在儿科重症监护室 (PICU) 住院的细菌性肺炎,证明了自动图表审查的潜力.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 儿科重症监护 儿科重症监护
背景情况:
- 在儿科重症监护室 (PICU) 准确识别细菌性肺炎对于及时治疗至关重要.
- 为了确认诊断,手动查看图表是耗时且资源密集的.
研究的目的:
- 评估定制生成预训练变压器 (GPT) -4o大语言模型 (LLM) 在PICU入院中识别细菌性肺炎的准确性.
- 评估与手册审查相比,LLM在分析临床笔记方面的速度和效率.
主要方法:
- 一项回顾性队列研究在一个48床的学术四级护理 PICU 中进行.
- 该GPT-4o模型是根据机构的肺炎诊断实践进行的,并分析了PICU入院前两天的临床记录.
- 虚拟儿科系统 (VPS) 注册表作为诊断的黄金标准.
主要成果:
- 在大约5分钟内,GPT-4o分析了来自3317名PICU患者的10,081条笔记.
- 该模型在分类与细菌性肺炎或没有细菌性肺炎的遭遇方面取得了94.8%的整体准确性.
- 在后期分析中,GPT-4o分类在71.8%的不一致病例中与盲目护理医生相匹配.
结论:
- 该GPT-4o LLM在诊断重症儿童的细菌性肺炎方面表现出高准确性和快速性能.
- 这项技术对自动化和加快临床表查看流程的发展有前途.
- 进一步开发类似的人工智能工具可以提高重症监护机构的诊断工作流程.
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