保护信息存在:缺失的数据和计算策略如何影响基于AI的早期预警得分的表现
Taeyong Sim1, Sangchul Hahn1, Kwang-Joon Kim1,2
1AITRICS Corp., Seoul 06221, Republic of Korea.
Journal of clinical medicine
|April 12, 2025
概括
在VitalCare-Major Adverse Event Score (VC-MAES) 的人工智能模型中,在预测患者病情恶化方面表现强. 它的准确性在完整的临床数据下最高,而像MICE这样的归算方法降低了它的有效性.
科学领域:
- 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
- 预测分析是一种预测分析.
背景情况:
- 数据的可用性对基于AI的早期预警分数 (EWS) 的表现产生重大影响.
- 维他卡尔重大不良事件评分 (VC-MAES) 是基于AI的EWS,使用缺失值的归算.
- 评估归算策略对于可靠的AI驱动的临床恶化预测至关重要.
研究的目的:
- 评估缺失数据范围和归算方法如何影响VC-MAES的预测性能.
- 在各种数据场景下比较VC-MAES与传统EWS的性能.
- 确定基于AI的EWS的最佳数据处理策略.
主要方法:
- 来自凯大学东山医院的6039名患者遭遇的分析.
- 评估VC-MAES的表现: 1) 只有生命体征和年龄, 2) 完整的临床变量, 3) 平均归算和 4) 通过链式方程 (MICE) 进行多重归算.
- 接收器运行特征曲线 (AUROC) 下的面积与传统的EWS的比较.
主要成果:
- 在有限的数据 (生命体,年龄) 的情况下,VC-MAES实现了0.896的AUROC,超过了传统的EWS (NEWS:0.797,MEWS:0.722).
- 完整的临床数据改善了VC-MAES AUROC,达到0.918.
- 与默认归算相比,平均归算 (0.885) 和MICE (0.827) 的结果是性能较低.
结论:
- VC-MAES表现出强大的预测能力,甚至在最小输入的情况下,其性能优于传统的EWS.
- 实际的临床数据集成显著提高基于AI的EWS准确性.
- 像平均计数或MICE这样的推算策略可能会降低性能,强调需要考虑缺失模式和上下文.
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