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.