一个大型的语言模型方法来评估功能状态尺度.
Blake Martin1,2,3, Anna M Janas1,3, Kristen R Miller1,3
1Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO.
概括
一个微调的人工智能 (AI) 模型,FSS-AI,可以估计功能状态量表 (FSS) 评分在重症儿童. 人工智能显示了与手动得分的中度一致,并可以识别正常与异常的FSS.
科学领域:
- 人工智能在医学中的应用
- 儿科重症监护 儿科重症监护
- 医疗信息学 医疗信息学
背景情况:
- 评估重症儿童的功能状况对于护理规划和结果评估至关重要.
- 功能状态量表 (FSS) 是一个常见的工具,但手动评分可能是耗时和主观的.
- 利用像GPT-4o这样的先进人工智能为自动化和高效的FSS评分提供了潜力.
研究的目的:
- 开发和评估一个微调的生成预训练变压器 (GPT) -4o人工智能 (AI) 模型,命名为FSS-AI,用于估计重症儿童的FSS分数.
- 为了评估人工智能生成的FSS得分的准确性和一致性,与不同住院时间点手动分配的得分相比.
主要方法:
- 对机械通风儿童 (1个月至18岁) 的预期队列进行了二次分析.
- 从基线,PICU转移和出院的患者笔记被用于训练和测试定制GPT-4o模型 (FSS-AI).
- 通过将其生成的得分与使用权重的科恩卡帕和准确度指标前性确定的手动FSS得分进行比较,评估了FSS-AI的性能.
主要成果:
- FSS-AI分析了428名患者的笔记,证明了与手动FSS得分在基线 (Kappa=0.59) 和出院 (Kappa=0.51) 中的中度一致.
- 该模型显示正常和异常FSS得分之间的区别很好,在基线时准确度最高 (0.90) 和PPV (0.95).
- 在出院时,FSS-AI以75%的准确度和56%的灵敏度识别出了新的发病率.
结论:
- 一个定制的GPT-4o模型 (FSS-AI) 可以有效地估计各种住院阶段的重症儿童的FSS得分.
- 人工智能工具显示了与手动评分的适度一致,并且可以区分正常和异常的功能状态.
- FSS-AI展示了其作为评估功能状态和识别儿科重症监护机构新病态的有效工具的潜力.
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