融合生命体征与病历文本，用深度学习提前24小时预测重症患者恶化风险。
Multimodal Deep Learning for Early Prediction of Patient Deterioration in the ICU: Integrating Time-Series EHR Data with Clinical Notes
- 用双向LSTM和ClinicalBERT分别处理生理数据与临床文本，通过跨模态注意力融合。
- 在超500万样本上测试，模型AUROC达0.7857，比仅用结构化数据提升2.5个百分点。
- 首次系统综述31项研究，证明结合病历文本能显著提升预警效果，适合临床研发参考。
早期识别重症监护室（ICU）患者临床恶化风险仍是重大挑战。对不良事件（包括死亡、使用血管活性药物、机械通气）的延迟识别会导致可预防的发病率和死亡率。本文提出一种多模态深度学习方法，将时序性结构化数据（生命体征与检验值）与非结构化临床病历文本结合，预测患者未来24小时内是否恶化。基于MIMIC-IV数据库，构建了74,822例ICU住院患者队列，生成570万小时级预测样本。模型采用双向LSTM编码器捕捉生理数据时序模式，以ClinicalBERT提取病历文本嵌入，并通过跨模态注意力机制融合。同时开展系统综述，梳理2015至2024年间31项相关研究。多数现有模型仅依赖结构化数据，AUC在0.70至0.85之间；而结合临床文本的研究虽少但潜力显著。本模型在823,641个保留样本上测试，获得0.7857的测试AUROC与0.1908的AUPRC，验证到测试差距仅0.6个百分点。消融实验证明：引入临床文本使AUROC提升2.5个百分点，AUPRC相对提升39.2%；深度学习模型持续优于传统方法（XGBoost AUROC: 0.7486，逻辑回归: 0.7171）。本工作既提供领域综述，也贡献可复现的多模态预测框架。
原文摘要 · Abstract (English)
Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU) remains a critical challenge. Delayed recognition of impending adverse events, including mortality, vasopressor initiation, and mechanical ventilation, contributes to preventable morbidity and mortality. We present a multimodal deep learning approach that combines structured time-series data (vital signs and laboratory values) with unstructured clinical notes to predict patient deterioration within 24 hours. Using the MIMIC-IV database, we constructed a cohort of 74,822 ICU stays and generated 5.7 million hourly prediction samples. Our architecture employs a bidirectional LSTM encoder for temporal patterns in physiologic data and ClinicalBERT embeddings for clinical notes, fused through a cross-modal attention mechanism. We also present a systematic review of existing approaches to ICU deterioration prediction, identifying 31 studies published between 2015 and 2024. Most existing models rely solely on structured data and achieve area under the curve (AUC) values between 0.70 and 0.85. Studies incorporating clinical notes remain rare but show promise for capturing information not present in structured fields. Our multimodal model achieves a test AUROC of 0.7857 and AUPRC of 0.1908 on 823,641 held-out samples, with a validation-to-test gap of only 0.6 percentage points. Ablation analysis validates the multimodal approach: clinical notes improve AUROC by 2.5 percentage points and AUPRC by 39.2% relative to a structured-only baseline, while deep learning models consistently outperform classical baselines (XGBoost AUROC: 0.7486, logistic regression: 0.7171). This work contributes both a thorough review of the field and a reproducible multimodal framework for clinical deterioration prediction.
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