机器学习和护士预测医院入院情况的比较在多站点紧急护理系统中
Jonathan Nover1, Matthew Bai1,2, Prem Tismina3
1Department of Emergency Medicine, Mount Sinai Health System, New York, NY.
Mayo Clinic proceedings. Digital health
|August 12, 2025
概括
机器学习模型准确地预测医院入院,超过护士预测. 整合护士输入并没有提高机器学习模型在招生决策中的性能.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 鉴定护士在预测住院患者方面发挥着至关重要的作用.
- 机器学习 (ML) 模型为改善医疗保健环境中的预测准确性提供了潜力.
- 评估ML模型与人类专业知识的比较性能对于临床采用至关重要.
研究的目的:
- 将护士预测的准确性与医院入院的ML模型进行前性比较.
- 为了确定是否结合护士预测与ML输出可以提高预测性能.
主要方法:
- 一项前性观察性研究在一个大型急诊室 (ED) 系统内的6家医院进行.
- 选护士对成年患者进行了二元入院预测.
- 一个整体ML模型 (XGBoost + Bio-Clinical BERT) 在历史数据上受训,并在前性数据上进行测试,包括护士预测.
主要成果:
- 与护士预测 (81.6%) 相比,ML模型的准确性更高 (85.4%).
- 在0.30的概率值下,ML模型表现出优异的灵敏度 (70.8%) 和特异性 (85.7%).
- 将护士预测与ML模型相结合并没有显著改善预测准确度.
结论:
- 基于ML的医院入院预测比分拣护士的估计更准确.
- 一个基于ML的入院预测系统可以可靠地利用分类时可用的数据.
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