在肺炎患者的ICU死亡率的基于机器学习的预测
Eun-Tae Jeon1, Hyo Jin Lee2, Tae Yun Park2
1Department of Radiology, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, 5 Gil 20, Boramae-Road, Dongjak-gu, Seoul, South Korea.
Scientific reports
|July 17, 2023
概括
机器学习模型在预测重症监护室 (ICU) 严重肺炎患者的死亡率方面明显优于传统的评分系统,为患者的结果提供了更高的准确性.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 传统的疾病严重性评分系统在预测重症监护室 (ICU) 严重肺炎病例的死亡率方面表现不佳.
- 准确的死亡率预测对于及时干预和重症监护机构的资源分配至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测重症监护室中严重肺炎患者的死亡率.
- 将ML模型的预测性能与传统评分系统进行比较.
主要方法:
- 一项回顾性研究,涉及816名因严重肺炎入院ICU的患者 (2016年1月至2021年12月).
- 评估了三种ML模型:与L2调节的逻辑回归,梯度增强决策树 (LightGBM) 和多层感知子 (MLP).
- 预测性能使用接收机操作特征曲线 (AU-ROC) 下的面积和净重新分类改进 (NRI) 来评估.
主要成果:
- 所有开发的ML模型在预测ICU死亡率方面显著优于简化急性生理学评分II (SAP S II) (ML模型的AU-ROC范围从0.820到0.838,而SAP S II的0.650;P<0.001).
- 与后勤回归相比,LightGBM和MLP模型在各种患者子组中展示了优越的重新分类能力.
- ML模型在各种各样的子组中显示出出色的预测性能,包括不同长度的ICU停留,年龄组,严重程度得分和治疗方式.
结论:
- 机器学习模型在预测重症监护室内严重肺炎患者的死亡率方面表现出色.
- 该研究强调了机器学习模型在提高重症监护中的死亡率预测准确度方面的潜力,比传统的评分系统提供了优势.
- 个别的ML模型可能为特定的患者子组提供量身定制的预测优势,需要进一步调查.
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