Multimodal Health Risk Prediction System for Chronic Diseases via Vision-Language Fusion and Large Language Models
This work addresses the need for a unified AI framework to predict health risks for patients with chronic diseases, though it appears incremental as it builds on existing visual-linguistic models.
The paper tackled the problem of predicting chronic disease health risks from multimodal clinical data by proposing VL-RiskFormer, a hierarchical multimodal Transformer with an LLM inference head, achieving an average AUROC of 0.90 and an expected calibration error of 2.7% on the MIMIC-IV cohort.
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.