用多模态融合模型提前预测儿童心脏骤停风险
Early Risk Prediction of Pediatric Cardiac Arrest from Electronic Health Records via Multimodal Fused Transformer
- 将电子病历的表格和文本数据融合，用Transformer捕捉动态风险模式
- 在儿童重症数据库上优于10种模型，五项指标表现更优
- 可识别临床有意义的风险因素，适合重症监护场景使用
早期预测儿童心脏骤停（CA）对高危重症监护环境中的及时干预至关重要。本文提出PedCA-FT，一种基于Transformer的新型框架，将电子健康记录（EHR）的表格视图与提取的文本视图融合，充分挖掘高维风险因素及其动态交互。通过为每种模态分别设计Transformer模块，该模型能捕捉复杂的时序与上下文模式，生成稳健的CA风险评估。在来自乔亚儿童重症监护室（CHOA-CICU）数据库的精选儿科队列上评估，本方法在五项关键性能指标上超越其他十种人工智能模型，并识别出具有临床意义的风险因素。这些结果表明，多模态融合技术有望提升早期心脏骤停检测能力，改善患者护理。
原文摘要 · Abstract (English)
Early prediction of pediatric cardiac arrest (CA) is critical for timely intervention in high-risk intensive care settings. We introduce PedCA-FT, a novel transformer-based framework that fuses tabular view of EHR with the derived textual view of EHR to fully unleash the interactions of high-dimensional risk factors and their dynamics. By employing dedicated transformer modules for each modality view, PedCA-FT captures complex temporal and contextual patterns to produce robust CA risk estimates. Evaluated on a curated pediatric cohort from the CHOA-CICU database, our approach outperforms ten other artificial intelligence models across five key performance metrics and identifies clinically meaningful risk factors. These findings underscore the potential of multimodal fusion techniques to enhance early CA detection and improve patient care.
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