通过可解释的机器学习预测急性临床恶化,以支持紧急护理决策
Stelios Boulitsakis Logothetis1, Darren Green2,3, Mark Holland4
1Department of Computer Science, University of Durham, Durham, UK.
Scientific reports
|August 21, 2023
概括
机器学习模型可以比NEWS2更准确地预测急诊室患者的病情恶化. 这些先进的算法改善了早期风险识别,可能减少错过的关键病例和警报疲劳.
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 临床决策支持
背景情况:
- 应急部门 (EDs) 面临着越来越大的运营压力.
- 有效地识别患有急性恶化的迫在眉风险的患者至关重要.
- 像国家早期预警分数2 (NEWS2) 这样的现有方法也有局限性.
研究的目的:
- 系统地比较机器学习算法 (逻辑回归,梯度增强决策树,支持矢量机器) 以预测即将发生的临床恶化.
- 通过使用真实世界患者数据,与NEWS2对模型性能进行评估.
- 整合可解释和公平意识的机器学习技术.
主要方法:
- 利用了来自英国萨尔福德皇家医院的118,886例无计划入院的数据集.
- 应用机器学习模型从医院入院时的电子病历 (EPR) 截面患者数据.
- 通过住院死亡率和/或在24小时内进入重症监护室来测量临床恶化.
- 采用形状添加式解释 (SHAP) 进行模型解释性和公平性评估.
主要成果:
- 与NEWS2相比,机器学习模型表现出更高的性能,平均精度增加了0.366%.
- 实现了每日警报率的显著降低 (高达[公式:参见文本]的下降).
- 在不同年龄和性别的人口统计学方面显示了差异偏差放大中位数0.599的减少.
- SHAP分析证实了模型预测与临床领域知识的一致性.
结论:
- 机器学习模型为预测患者ED恶化提供了有希望的进步.
- 这些模型有潜力减少警觉疲劳,并识别高风险的患者错过了当前的得分.
- 需要进一步的临床试验才能将这些数据驱动的风险建模工具纳入实践.
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