使用生命体征和机器学习早期识别进入急诊室的高风险患者
Qingyuan Liu1, Yixin Zhang2, Jian Sun3
1School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China.
World journal of emergency medicine
|March 26, 2025
概括
使用初始生命体征的深度学习模型准确地识别高风险急诊室患者. 这种多层感知子模型显示了改善患者分拣中的临床决策的前景.
科学领域:
- 紧急医疗 紧急医疗
- 临床信息学 临床信息学
- 医疗保健中的机器学习
背景情况:
- 在急诊室准确识别高风险患者对于资源分配和患者的治疗结果至关重要.
- 使用初始生命体征的早期预测模型可以显著帮助ED分拣.
- 这项研究的重点是开发和验证这样的预测模型.
研究的目的:
- 开发和评估一个早期预测模型,用于识别ED中高风险患者.
- 将多层感知子 (MLP) 模型的性能与传统方法和分数进行比较.
- 确定高风险患者状态的关键生命体征预测因子.
主要方法:
- 使用中国紧急诊断,评估和治疗 (CETAT) 数据库 (2020年1月 - 2023年6月) 的回顾性队列研究.
- 分析了38797名患者的初始生命体征.
- 机器学习模型的评估,包括MLP,逻辑回归和高斯天真贝叶斯,使用接收器操作特征曲线下的面积 (AUC-ROC).
主要成果:
- 18.2%的分析患者被确定为高风险患者.
- 该MLP模型实现了最高的AUC-ROC (0.717-0.738),超过了后勤回归,高斯天真贝叶斯和国家早期预警得分 (NEWS).
- 昏迷状态,SPO2和静脉压是MLP模型中最重要的预测因素,昏迷状态是最有影响的.
结论:
- 使用初始生命体征的MLP模型表明,对高风险ED患者的预测准确度更高.
- 这种模型有可能在ED分拣过程中显著增强临床决策.
- 最初的生命体征是紧急护理中早期风险分层的强有力的指标.
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