通过人工智能检测患有痴呆风险的急诊室患者
Inessa Cohen1,2,3, Richard Andrew Taylor1,2, Haipeng Xue1
1Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
概括
紧急部门痴呆症算法 (EDDA) 通过使用电子健康记录有效地检测老年人的痴呆症. 这种机器学习工具有助于急诊室的临床医生识别未被诊断的痴呆病例.
科学领域:
- 老年学是一门学科.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 对于老年人来说,在急诊室 (ED) 检测痴呆症至关重要.
- 现有的方法可能无法充分支持ED环境中的临床决策.
研究的目的:
- 开发和验证急诊室痴呆症算法 (EDDA).
- 加强痴呆症检测和支持ED中老年人 (65岁以上) 的临床决策.
主要方法:
- 一项回顾性研究使用电子健康记录数据分析了759,665次ED访问.
- 训练有监督和半无监督的机器学习模型 (XGBoost,随机森林,LASSO).
- 验证是在一个单独的测试集上进行的,该测试集包括400次ED接触,并进行了判定图表审查.
主要成果:
- 在测试组中,EDDA获得了0.85的AUROC,在验证组中达到0.93.
- 积极无标记的学习增强了算法性能.
- 在EDDA和临床医生诊断之间观察到中度一致 (kappa = 0.50),17%的EDDA阳性患者患有未诊断的可能痴呆症.
结论:
- 欧洲痴呆症诊断局 (EDDA) 显示了改善急诊室痴呆症检测的巨大潜力.
- 实时实施EDDA可以提高患者的治疗结果和护理过渡.
- 该算法旨在实现易用,优化ED工作流程并支持临床医生.
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