多模式学习用于预测住院时间长度
Junde Chen1, Yuxin Wen1, Michael Pokojovy2
1Dale E. and Sarah Ann Fowler School of Engineering, Chapman University, Orange, CA, 92866, USA.
Computers in biology and medicine
|February 21, 2024
概括
这项研究引入了一种新的数据融合模型 (DF-Mdl),利用各种临床数据预测住院时间 (LoS). DF-Mdl有效地整合了多模式患者信息,优于现有的方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据科学 临床数据科学
背景情况:
- 准确预测住院时间 (LoS) 对医院的运营效率和资源管理至关重要.
- 利用异构的患者数据 (人口统计,临床记录,实验室,图像) 来进行LoS预测,由于数据的复杂性和多样性,因此存在重大挑战.
- 现有的预测模型往往难以有效地整合多模式的临床数据来准确地预测LoS.
研究的目的:
- 开发和验证一种新的数据融合模型 (DF-Mdl),用于准确的住院病人的LoS预测.
- 有效地将异构的临床数据源,包括医疗图像,文本和数字数据集成到统一的预测框架中.
- 为了证明拟议的DF-Mdl在LoS预测中优于最先进的方法.
主要方法:
- 开发了一个多模数据融合模型 (DF-Mdl),集成了不同数据类型的专门子模型.
- 一个卷积神经网络 (CNN) 模型 (CRXMDL) 设计用于胸部X射线 (CXR) 图像分析.
- 长期短期记忆网络 (LSTM) 用于临床文本特征提取,以及注重嵌入的1D CNN用于数值数据分析.
- 这些子模型被整合起来,形成最终的DF-Mdl,用于预测住院患者的LoS.
主要成果:
- 拟议的DF-Mdl在LoS预测的MIMIC-IV测试数据集上获得了最佳的R2 (0.6039) 和E_VAR (0.6042) 值.
- 经验结果表明,与其他最先进的 (SOTA) 方法相比,性能优越.
- 该模型在整合异质数据以准确预测LoS的有效性得到了验证.
结论:
- 新型数据融合模型 (DF-Mdl) 有效地整合了异质的临床数据,以准确地预测住院患者的LoS.
- 拟议的方法显示了改善医院管理和服务效率的重大前景.
- 该DF-Mdl代表了在住院长度临床预测建模中的可行和有效的进步.
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