基于机器学习的模型用于预测社区,护理人员和医院阶段的危急疾病
Sijin Lee1, Hyun Ji Park2, Jumi Hwang2
1Department of Emergency Medicine, Korea University, College of Medicine, Seoul, Republic of Korea.
Emergency medicine international
|July 5, 2023
概括
机器学习模型可以早期预测危急疾病,改善患者流动和资源配置. 这些模型在到达急诊室之前准确地识别高风险患者.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 紧急医疗 紧急医疗
背景情况:
- 紧急部门 (ED) 的过度拥挤使医疗保健系统受到压力,并影响患者的治疗结果.
- 早期识别重症患者对于有效的患者流和资源管理至关重要.
- 预测模型可以帮助医院前和医院内分类.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测严重疾病.
- 用韩国国家紧急情况部门信息系统 (NEDIS) 数据开发了社区,护理人员和医院阶段的模型.
- 评估随机森林和LightGBM算法在危急疾病预测中的性能.
主要方法:
- 利用了韩国国家紧急情况部门信息系统 (NEDIS) 的数据.
- 使用随机森林和光梯度增强机 (LightGBM) 算法开发了预测模型.
- 使用接收器操作特征曲线 (AUROC) 下面的面积来评估模型性能.
主要成果:
- 随机森林模型实现的AUROC为0.870 (社区),0.897 (护理人员) 和0.950 (医院).
- 轻GBM模型的AUROC为0.877 (社区),0.899 (护理人员) 和0.950 (医院).
- 两种ML模型在所有阶段都表现出高的预测性能.
结论:
- 机器学习模型有效地使用特定阶段的变量预测严重疾病.
- 这些模型可以指导患者分组,并根据疾病严重程度促进适当的医院分配.
- 进一步开发模拟模型可以优化有限的医疗资源的分配.
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