开发和外部验证一个检测模型,以追溯识别急性呼吸困扰综合征患者
Elizabeth Levy1,2,3, Dru Claar4, Ivan Co4,5
1Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Critical care medicine
|April 8, 2025
概括
机器学习模型可以有效地使用电子健康记录 (EHR) 数据识别急性呼吸困扰综合征 (ARDS) 的患者. 这种方法可以在各个医疗机构中追溯识别ARDS.
科学领域:
- 计算医学是一种计算医学.
- 医疗信息学 医疗信息学
- 关键护理医学 关键护理医学
背景情况:
- 诊断急性呼吸困扰综合征 (ARDS) 可能具有挑战性,影响及时治疗.
- 电子健康记录 (EHR) 包含大量数据,可能有助于识别ARDS.
- 开发用于ARDS识别的自动化方法对于临床研究和患者护理至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于追溯识别ARDS患者.
- 使用EHR数据,包括结构化数据和放射学报告,用于ARDS分类.
- 在不同机构中对模型的性能进行外部验证.
主要方法:
- 追溯性队列研究,涉及医生判决的ARDS病例.
- 使用生命体征,呼吸辅助,实验室,药物,放射学报告和临床笔记来训练ML模型.
- 使用AUROC,ICI,灵敏度,特异性和PPV等指标进行内部和外部验证.
主要成果:
- 性能最好的模型整合了结构化的EHR数据和放射学报告.
- 外部验证显示AUROC为0.88和ICI为0.13.
- 该模型实现了80%的灵敏度和64%的PPV,在满足标准后的2.2小时中位数确定了ARDS.
结论:
- 分析EHR数据的机器学习模型对于追溯ARDS识别是有效的.
- 开发的模型证明了在不同的医疗保健系统中具有普遍性.
- 这种方法有助于在临床实践中有效和准确地确定ARDS病例.
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