Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation
Keywords: Multi-modal clinical data, latent diffusion model, chest X-ray image, eletronic health records.
TL;DR: We propose DDL-CXR to address the asynchronicity in clinical multimodal fusion by generating individualized up-to-date CXR images. Cross-modal interactions are captured by the generation process, leading to improved prediction performance.
Abstract: Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost and radiation dose. When clinical prediction is needed, the last available CXR image might have been outdated, leading to suboptimal predictions. To address this challenge, we propose DDL-CXR, a method that dynamically generates an up-to-date latent representation of the individualized CXR images. Our approach leverages latent diffusion models for patient-specific generation strategically conditioned on a previous CXR image and EHR time series, providing information regarding anatomical structures and disease progressions, respectively. In this way, the interaction across modalities could be better captured by the latent CXR generation process, ultimately improving the prediction performance. Experiments using MIMIC datasets show that the proposed model could effectively address asynchronicity in multimodal fusion and consistently outperform existing methods.
Primary Area: Machine learning for healthcare
Submission Number: 18136
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