Multimodal models play a critical role in mortality prediction for ICU patients.

Traditional emergency patient outcome models rely solely on structured data, overlooking critical insights from unstructured text (e.g., medical notes). We develop a multimodal framework integrating structured clinical metrics with natural language processing (NLP) of text data to predict ED outcomes. Compared to structured-only models, the multimodal approach improves the performance for mortality prediction, with NLP features enhancing interpretability. Combining structured and unstructured data captures holistic patient profiles, demonstrating that multimodal modeling boosts predictive accuracy in emergency care. These findings advocate for routine NLP integration in ED prediction systems.

传统急诊患者预后模型仅依赖结构化临床数据，却忽略了非结构化文本（如医疗病历笔记）所蕴含的关键临床信息。本研究开发了一种多模态框架，将结构化临床指标与文本数据的自然语言处理（NLP）技术相结合，用于预测急诊（ED）结局。相较于仅使用结构化数据的模型，该多模态方法可提升死亡率预测性能，且NLP特征还能增强模型的可解释性。结合结构化与非结构化数据能够构建全面的患者画像，证明多模态建模可提升急诊诊疗场景下的预测准确性。上述研究结果支持在急诊（ED）预测系统中常规集成NLP技术。