预测护理需要使用生命体征和生物标志物的代理数据:深度学习模型的应用
Yunmi Baek1, Kihye Han2, Eunjoo Jeon3
1Department of Nursing, Hallym University Medical Center, Gyeonggi-do, South Korea.
Journal of clinical nursing
|December 9, 2024
概括
深度学习模型,包括循环神经网络 (RNN) 和长期短期记忆 (LSTM),有效地预测住院患者的护理需求. 这些先进模型的性能优于传统的回归,改善了护理工作量和人员决策.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 护理管理的护理管理.
背景情况:
- 护理管理中的深度学习应用,用于工作负载管理是有限的.
- 准确预测患者护理需求对于有效的人员配置和护理服务至关重要.
研究的目的:
- 开发和比较深度学习模型 (RNN,LSTM) 与传统的回归模型,用于预测住院患者的护理需求代理.
- 评估预测模型在提高护理资源分配方面的有效性.
主要方法:
- 对20855名成年患者的电子病历进行了横截面二次数据分析.
- 使用了前48小时的患者数据 (生命体征,生物标志物,人口统计数据).
- 采用重复神经网络 (RNN) 和长期短期记忆 (LSTM) 算法进行序列数据处理.
主要成果:
- 与传统回归相比,RNN和LSTM模型对护理需求代理的预测性能都表现出卓越的预测性能.
- 在患者病情快速变化的时期,观察到预测准确度的降低.
结论:
- 循环神经网络 (RNN) 和长期短期记忆 (LSTM) 模型为护理需求提供了增强的预测能力.
- 积极应用这些深度学习模型可以帮助及时检测患者需求,支持有效和安全的护理服务.
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