人工智能驱动的儿童急诊室受伤报告
Devin Singh1,2,3, Alper Celik4, Evangeline W J Zhang1
1Division of Pediatric Emergency Medicine, Hospital for Sick Children (SickKids), Toronto, Ontario, Canada.
JAMA network open
|July 31, 2025
概括
自然语言处理 (NLP) 变压器模型可以在急诊室 (ED) 记录中自动检测受伤病例. 这有助于改善公共卫生监测,减少手动审查,并使数据分析更快.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 伤害监测对于预防儿童发病率和死亡率至关重要.
- 手动审查急诊室 (ED) 创伤跟踪记录是耗时的,并且由于患者数量大而紧张.
- 及时的数据分析受到损害,延迟了对公共卫生风险的检测.
研究的目的:
- 评估自然语言处理 (NLP) 变压器模型,用于在ED医疗记录中自动检测受伤病例.
- 评估提高加拿大医院伤害报告和预防计划 (CHIRPP) 工作流程效率的潜力.
- 在识别受伤相关病例时保持高度敏感.
主要方法:
- 一项预后研究分析了从2017年到2023年在三级儿科中心的217,173次儿科ED访问.
- 两个基于变压器的NLP模型 (DistilBERT和BERT-large) 使用监督学习进行了微调.
- 模型对CHIRPP报告的医疗记录进行了分类,结果包括真实阳性率,曲线下的面积和手动审查的减少.
主要成果:
- 这两种NLP模型都表现出高精度,AUROC为0.983和AUPRC约为0.93.
- 模型实现了0.90的真实阳性率,识别了90%的受伤病例.
- 手动审查的负担从100%减少到17%的ED访问,显著提高效率.
结论:
- NLP变压器模型有效地将ED医疗记录中的伤害病例检测自动化.
- 这种自动化有可能实现实时伤害监控监控.
- 这些发现表明,公共卫生风险检测和响应能力的显著改善.
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