Multi-Modal Deep Learning for Predicting Patient Outcomes in Intensive Care Units

Abstract

Predicting patient outcomes in Intensive Care Units (ICUs) is a critical task that can guide clinical decision-making and improve patient management. However, the heterogeneous nature of ICU data, encompassing various modalities such as vital signs, laboratory results, clinical notes, and imaging, poses significant challenges. This paper proposes a multi-modal deep learning framework to predict patient outcomes in ICUs by integrating diverse data sources. Our approach employs convolutional neural networks (CNNs) for imaging data, recurrent neural networks (RNNs) for sequential data like time-series vital signs, and natural language processing (NLP) techniques for unstructured clinical notes. By combining these modalities, the proposed model learns comprehensive patient representations, improving the accuracy of outcome predictions, including mortality, length of stay, and need for mechanical ventilation. The results show that the multi-modal approach significantly outperforms traditional single-modal models, demonstrating the potential of deep learning to enhance predictive analytics in critical care settings.