一种深度学习方法用于住院长度和死亡率预测
Junde Chen1, Trudi Di Qi1, Jacqueline Vu1
1Fowler School of Engineering, Chapman University, Orange 92866, CA, USA.
Journal of biomedical informatics
|October 18, 2023
概括
本研究介绍了1D-MSNet,这是一种用于预测重症监护室 (ICU) 停留时间 (LoS) 和死亡率的新型深度学习模型. 该模型在MIMIC-IV数据集上表现出卓越的性能,改善了医疗保健管理.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 准确预测重症监护室 (ICU) 患者的结果对于医院资源管理和护理质量至关重要.
- 在实时需求容量 (RTDC) 管理中预测停留时间 (LoS) 和死亡率的帮助.
研究的目的:
- 开发和评估一种新的深度学习模型,1D-MSNet,用于预测ICU LoS和死亡率.
- 通过使用多尺度卷积神经网络架构来提高预测准确性.
主要方法:
- 提出了一个新的1D多尺度卷积神经网络 (1D-MSNet) 架构.
- 整合了一个atrous因果空间金字塔聚合模块,用于特征提取.
- 使用合成少数过量采样技术 (SMOTE) 的焦点损失来解决类不平衡.
主要成果:
- 在MIMIC-IV数据集上实现了对LoS预测的最佳R-Square (0.57) 和RMSE (3.61).
- 在死亡率预测方面达到97.73%的高测试准确度.
- 与现有最先进的方法相比,表现出优越的性能.
结论:
- 拟议的1D-MSNet有效地预测了ICU的LOS和死亡率.
- 该模型在临床决策支持和医院管理方面取得了重大进展.
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