用于改进30天全因再入院预测的多模态时空图神经网络
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
AI总结：
提出多模态时空图神经网络MM-STGNN融合多模态住院纵向数据预测30天全因再入院，在双中心数据上AUROC达0.79，显著优于临床标准LACE+，且模型可解释性强、模态无关。
摘要：
预测30天再入院的措施被认为是医院的重要质量因素，因为准确的预测可以通过在患者出院前识别高风险患者来降低整体护理成本。尽管最近基于深度学习的研究在再入院预测方面显示出有希望的经验结果，但仍存在一些可能阻碍广泛临床应用的局限性，例如：(a)仅考虑患有特定疾病的患者，(b)现有方法未利用数据的时间性，(c)假设个体入院相互独立，这是不现实的，(d)先前的研究通常局限于单一数据源和单中心数据。为了解决这些局限性，我们提出了一种多模态、模态无关的时空图神经网络(MM-STGNN)，用于预测30天全因医院再入院，该网络融合了多模态住院患者纵向数据。通过使用来自两个独立中心的纵向胸部X光片和电子健康记录来训练和评估我们的方法，我们证明MM-STGNN在主要和外部数据集上均达到0.79的AUROC。此外，在主要数据集上，MM-STGNN显著优于当前临床参考标准LACE+评分(AUROC=0.61)。对于患有心脏和血管疾病的亚组患者，我们的模型在预测30天再入院方面也优于基线方法(例如，心脏病患者的AUROC提高了3.7个百分点)。最后，定性模型可解释性分析表明，虽然患者的原发诊断未被明确用于训练模型，但对模型预测至关重要的节点特征直接反映了患者的原发诊断。重要的是，我们的MM-STGNN与节点特征模态无关，可用于整合多模态数据，以便在各种下游资源分配任务中对患者进行分诊。
英文摘要：
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged. While recent deep learning-based studies have shown promising empirical results on readmission prediction, several limitations exist that may hinder widespread clinical utility, such as (a) only patients with certain conditions are considered, (b) existing approaches do not leverage data temporality, (c) individual admissions are assumed independent of each other, which is unrealistic, (d) prior studies are usually limited to single source of data and single center data. To address these limitations, we propose a multimodal, modality-agnostic spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission that fuses multimodal in-patient longitudinal data. By training and evaluating our methods using longitudinal chest radiographs and electronic health records from two independent centers, we demonstrate that MM-STGNN achieves AUROC of 0.79 on both primary and external datasets. Furthermore, MM-STGNN significantly outperforms the current clinical reference standard, LACE+ score (AUROC=0.61), on the primary dataset. For subset populations of patients with heart and vascular disease, our model also outperforms baselines on predicting 30-day readmission (e.g., 3.7 point improvement in AUROC in patients with heart disease). Lastly, qualitative model interpretability analysis indicates that while patients' primary diagnoses were not explicitly used to train the model, node features crucial for model prediction directly reflect patients' primary diagnoses. Importantly, our MM-STGNN is agnostic to node feature modalities and could be utilized to integrate multimodal data for triaging patients in various downstream resource allocation tasks.
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