实时机器学习模型用于预测重症患者的短期死亡率:开发和国际验证
Leerang Lim1, Ukdong Gim2, Kyungjae Cho2
1Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Critical care (London, England)
|March 15, 2024
概括
一个新的实时机器学习模型准确地预测了重症患者的短期死亡率. 这种先进的模型,iMORS,在内部和外部验证方面表现出很高的表现,超过了像NEWS.这样的现有分数.
科学领域:
- 关键护理医学 关键护理医学
- 机器学习在医疗保健中的应用
- 对患者结果的预测建模.
背景情况:
- 危急病患者短期死亡率的实时预测对于及时干预至关重要.
- 现有的模型需要在不同的临床环境和种族中进行验证.
- 开发准确的预测模型对于改善重症监护室患者护理至关重要.
研究的目的:
- 开发和验证一套机器学习模型,用于预测重症患者的短期死亡率.
- 用例行收集的临床变量来评估模型的性能.
- 将模型的预测准确度与国家早期预警得分 (NEWS) 进行比较.
主要方法:
- 开发了一种组合模型,结合了深度学习和光梯度增强机器.
- 内部验证使用了来自韩国学术医院 (2007-2021) 的数据.
- 外部验证使用了来自MIMIC,eICU-CRD和阿姆斯特丹UMCdb.db 的大型数据集.
主要成果:
- 开发的模型 (iMORS) 实现了0.964的高内部AUROC和0.870到0.890.89的外部AUROC.
- 在所有验证数据集中,iMORS的表现明显优于NEWS (p < 0.001).
- 该模型在多样化的国际队列中展示了强大的预测性能.
结论:
- 该iMORS模型提供了优秀的实时预测,在危急病患者的短期死亡率.
- 在内部和外部验证后,该模型显示了临床应用的巨大潜力.
- 这种机器学习工具可以作为重症监护病房临床医生的有价值的决策支持系统.
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