An Improved Multimodal Cirrhosis Prediction Method Through Microbiota Based on VQVAE

Liver cirrhosis, a critical and potentially fatal liver condition, often progresses to severe complications, including liver failure and hepatocellular carcinoma. Early and accurate detection of this disease is imperative for the implementation of effective clinical strategies. Traditional diagnostic approaches, which rely heavily on invasive liver biopsies and are subject to interpretive variability, present significant limitations. The gut microbiome serves as a dynamic biomarker, reflecting the internal milieu of the body and exhibiting a strong correlation with the onset and progression of liver cirrhosis. To enhance the detection of cirrhosis, we introduce a novel multimodal predictive model termed VQ-MMCM, which leverages multimodal microbiome data. By amassing gut microbiome data and creating a comprehensive training set, we have engineered the VQ-MMCM architecture, grounded in the vector quantized variational autoencoder (VQ-VAE). This model employs a VQ-VAE as the encoder's foundational structure and integrates a trainable weighted sum module to synthesize features, thereby achieving superior representation of microbiome data. Through supervised learning on a dataset of liver cirrhosis cases, augmented by pre-training on unlabeled data, the VQ-MMCM effectively harnesses both the abundance profile and marker profile derived from gut microbiota. This dual-modality approach surpasses the limitations of existing models, such as poor generalization and convergence challenges, and has demonstrated a recognition AUC score of 0.931 for liver cirrhosis.