MANGO通过多模态数据提升重症监护患者病情严重程度预测准确率
MANGO: Multimodal Acuity traNsformer for intelliGent ICU Outcomes
- 融合电子病历、可穿戴设备、面部表情和环境传感器数据，用Transformer建模跨模态关联
- 在预测病情变化和生命支持需求上，最佳模型AUROC达0.76（95%置信区间0.72-0.79）
- 适合关注重症监护智能预警与多源数据融合的临床研究者和医疗AI开发者
重症监护室（ICU）患者病情严重程度的评估对及时干预至关重要。人工智能技术的进步显著提升了严重程度预测的准确性。然而，以往基于机器学习的严重程度预测主要依赖电子健康记录（EHR），常忽略患者活动能力、环境因素及反映疼痛或躁动的面部线索等关键信息。为此，我们提出MANGO：面向智能ICU结局的多模态敏锐度变换器，旨在提升对患者严重程度状态、状态转变及生命支持治疗需求的预测能力。我们构建了包含四类关键模态的多模态数据集ICU-Multimodal，包括EHR数据、可穿戴传感器数据、患者面部表情视频以及环境传感器数据，并用于训练MANGO模型。该模型采用基于Transformer掩码自注意力机制的多模态特征融合网络，能够在部分模态缺失的情况下捕捉并学习不同数据源间的复杂交互关系。实验结果表明，多模态融合显著增强了模型在预测严重程度状态、状态转变及生命支持治疗需求方面的能力。最佳模型在预测严重程度状态转变及生命支持需求上的受试者工作特征曲线下面积（AUROC）达到0.76（95%置信区间：0.72–0.79），在严重程度状态预测中达到0.82（95%置信区间：0.69–0.89）。
原文摘要 · Abstract (English)
Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have significantly improved the accuracy of acuity predictions. However, prior studies using machine learning for acuity prediction have predominantly relied on electronic health records (EHR) data, often overlooking other critical aspects of ICU stay, such as patient mobility, environmental factors, and facial cues indicating pain or agitation. To address this gap, we present MANGO: the Multimodal Acuity traNsformer for intelliGent ICU Outcomes, designed to enhance the prediction of patient acuity states, transitions, and the need for life-sustaining therapy. We collected a multimodal dataset ICU-Multimodal, incorporating four key modalities, EHR data, wearable sensor data, video of patient's facial cues, and ambient sensor data, which we utilized to train MANGO. The MANGO model employs a multimodal feature fusion network powered by Transformer masked self-attention method, enabling it to capture and learn complex interactions across these diverse data modalities even when some modalities are absent. Our results demonstrated that integrating multiple modalities significantly improved the model's ability to predict acuity status, transitions, and the need for life-sustaining therapy. The best-performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.72-0.79) for predicting transitions in acuity status and the need for life-sustaining therapy, while 0.82 (95% CI: 0.69-0.89) for acuity status prediction...
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