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机器学习使用数字实验室结果进行短期所有原因的医院内死亡率预测

Gen Shimada^1,2, Rumi Nakabayashi², Yasuhiro Komatsu^3,4

¹Hernia Center, St. Luke's International Hospital, Tokyo, Japan.

|November 9, 2023

概括

机器学习模型使用实验室结果与关键值列表相比,显著改善了7天死亡率预测. 这一进步为医疗保健提供者提供了更安全,更及时的警报.

科学领域:

临床实验室科学临床实验室科学
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

关键值列表 (CVL) 是标准的临床医生警示关键的实验室结果.
尽管如此,CVL对死亡率的预测能力仍然不清楚.
对于危及生命的实验室偏差,立即采取行动至关重要.

研究的目的:

为了比较机器学习模型与传统CVL在预测患者7天死亡率方面的有效性.
用实验室数据评估 eXtreme渐变增强 (XGB) 模型的性能.

主要方法:

利用了来自92,673名患者的335,430个临床实验室结果 (2018年7月至2019年12月).
在最初的12个月数据的70%上使用XGB开发了一个7天死亡率预测模型.
将XGB模型和CVL与剩余的30%和随后的6个月数据集进行验证.

主要成果:

XGB模型的准确性明显高于CVL (p < 0.001).
接收机运行特征面积在曲线下XGB的值为0.9807和0.9646,而CVL则为0.7549和0.7172.
XGB模型在死亡率预测中实现了更高的真实阳性率和更低的虚假阳性率.

结论:

基于实验室结果的基于机器学习的死亡率预测显著超过了传统的CVL.
ML模型为及时的患者风险评估提供了更准确和潜在的更安全的方法.
这项技术可以增强临床决策和患者的结果.

关键词:

关键价值列表中的关键值列表.实验室测试试验实验室测试试验机器学习是机器学习.死亡率预测死亡率预测

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