Multimodal Health Risk Prediction System for Chronic Diseases via Vision-Language Fusion and Large Language Models

• URL: http://arxiv.org/abs/2509.18221v1
• Date: Mon, 22 Sep 2025 05:26:59 GMT
• Title: Multimodal Health Risk Prediction System for Chronic Diseases via Vision-Language Fusion and Large Language Models
• Authors: Dingxin Lu, Shurui Wu, Xinyi Huang,
• Abstract summary: There is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks.<n>We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer.
• Score: 6.169451756799087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rising global burden of chronic diseases and the multimodal and heterogeneous clinical data (medical imaging, free-text recordings, wearable sensor streams, etc.), there is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks. We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer. The system builds on the dual-stream architecture of existing visual-linguistic models (e.g., PaLM-E, LLaVA) with four key innovations: (i) pre-training with cross-modal comparison and fine-grained alignment of radiological images, fundus maps, and wearable device photos with corresponding clinical narratives using momentum update encoders and debiased InfoNCE losses; (ii) a time fusion block that integrates irregular visit sequences into the causal Transformer decoder through adaptive time interval position coding; (iii) a disease ontology map adapter that injects ICD-10 codes into visual and textual channels in layers and infers comorbid patterns with the help of a graph attention mechanism. On the MIMIC-IV longitudinal cohort, VL-RiskFormer achieved an average AUROC of 0.90 with an expected calibration error of 2.7 percent.

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