The Exposome Interpreter as a Multi-modal Framework for Autoimmune Trigger Identification

The prevalence of autoimmune diseases is rising, yet personalized treatment remains elusive due to a significant gap between the established knowledge that environmental exposures (exposome) contribute up to 70% of disease risk and the clinical ability to identify patient-specific triggers. This translational challenge is rooted in two fundamental information processing bottlenecks: a clinical data standardization bottleneck, where crucial patient biomarker and exposure data are fragmented across visually complex, semi-structured documents like PDF lab reports, and a biomedical knowledge synthesis bottleneck, where understanding the immunotoxic effects of thousands of chemicals requires synthesizing a vast and rapidly evolving body of scientific literature. Collectively, these bottlenecks preclude clinicians from manually reconciling a patient's unique immunological profile against the dynamic body of knowledge concerning environmental chemicals and their biological impacts. To address this challenge, we introduce the Exposome Interpreter, a multi-modal framework that infers evidence-based links between a patient's environmental exposures and their specific immunological dysregulation. The Exposome Interpreter is implemented through a modular, three-stage architecture. The first stage, Multi-modal Data Interpretation, formulates structured information extraction from heterogeneous clinical documents (reports, imagery) as a prediction task. We propose a hybrid VLM (Vision-Language Model) architecture that employs a large generalist model to generate programmatic weak supervision, which is then used to efficiently fine-tune a smaller, specialized model for high-precision key-value extraction. The second stage, Biomedical Knowledge Synthesis, constructs a dynamic knowledge base using a Retrieval-Augmented Generation (RAG) pipeline. This pipeline is optimized with specialized biomedical embeddings for high-fidelity retrieval from scientific literature, while a fine-tuned LLM performs multi-document synthesis to generate evidence-grounded causal hypotheses. Finally, the Personalization Engine frames trigger identification as a personalized ranking problem. It learns to score and rank potential exposome-biomarker links by integrating the structured patient profile with the synthesized knowledge, thereby prioritizing the most probable causal factors. The end-to-end performance is tested on link prediction in simulated clinical scenarios.