Multi-modal learning for inpatient length of stay prediction
Multi-modal learning for inpatient length of stay prediction
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DOI：
10.1016/j.compbiomed.2024.108121
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发表时间：
2024-02-20
影响因子：
7.7
通讯作者：
Moen，Scott
中科院分区：
文献类型：
--
作者：
Chen，Junde;Wen，Yuxin;Moen，Scott
预测住院患者的住院日对于医院提高服务效率、增强管理能力具有重要意义。患者医疗记录与LoS密切相关。然而，由于数据的多样性、异构性和复杂性，有效地利用这些异构数据来提出可以准确预测LoS的预测模型变得具有挑战性。为了应对这一挑战，本研究的目的是建立一种新的数据融合模型，称为DF-Mdl，整合异构的临床数据，预测住院患者出院和入院之间的LoS。多模态数据，如人口统计数据，临床笔记，实验室测试结果，和医学图像中使用我们提出的方法与单独的“基本”子模型分别适用于每个不同的数据模式。具体而言，卷积神经网络（CNN）模型，我们称之为CRXMDL，是为胸部X射线（CXR）图像数据设计的，两个长短期记忆网络用于从长文本数据中提取特征，并开发了一种新型的注意力嵌入式一维卷积神经网络，以从数值数据中提取有用的信息。最后，这些基本模型集成，形成一个新的数据融合模型（DF-Mdl）的住院LoS预测。在重症监护医学信息市场（MIMIC）-IV测试数据集上，所提出的方法在竞争对手中获得了最佳R2和EVAR值，分别为0.6039和0.6042。实验结果表明，与其他最先进的（SOTA）方法相比，该方法具有更好的性能，从而证明了该方法的有效性和可行性。
Predicting inpatient length of stay (LoS) is important for hospitals aiming to improve service efficiency and enhance management capabilities. Patient medical records are strongly associated with LoS. However, due to diverse modalities, heterogeneity, and complexity of data, it becomes challenging to effectively leverage these heterogeneous data to put forth a predictive model that can accurately predict LoS. To address the challenge, this study aims to establish a novel data-fusion model, termed as DF-Mdl, to integrate heterogeneous clinical data for predicting the LoS of inpatients between hospital discharge and admission. Multi-modal data such as demographic data, clinical notes, laboratory test results, and medical images are utilized in our proposed methodology with individual “basic” sub-models separately applied to each different data modality. Specifically, a convolutional neural network (CNN) model, which we termed CRXMDL, is designed for chest X-ray (CXR) image data, two long short-term memory networks are used to extract features from long text data, and a novel attention-embedded 1D convolutional neural network is developed to extract useful information from numerical data. Finally, these basic models are integrated to form a new data-fusion model (DF-Mdl) for inpatient LoS prediction. The proposed method attains the bestR2andEVARvalues of 0.6039 and 0.6042 among competitors for the LoS prediction on the Medical Information Mart for Intensive Care (MIMIC)-IV test dataset. Empirical evidence suggests better performance compared with other state-of-the-art (SOTA) methods, which demonstrates the effectiveness and feasibility of the proposed approach.