Automated Multi-Modal Anomaly Detection in Liquid Biopsy Data for Early-Stage Pancreatic Cancer Diagnosis

**Abstract:** This paper introduces a novel framework, the Automated HyperScore Evaluation System (AHES), integrating multi-modal data ingestion, semantic parsing, and probabilistic anomaly detection to enable early-stage diagnosis of pancreatic cancer via liquid biopsy analysis. AHES leverages recent advancements in natural language processing (NLP), knowledge graph construction, and statistical learning to create a robust and scalable solution for detecting subtle anomalies in circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and protein biomarkers, moving beyond traditional single-marker assessments. Combining rigorous algorithmic validation and a dynamic human-AI hybrid feedback loop, AHES aims to improve diagnostic accuracy and reduce false negatives, significantly impacting patient outcomes. The system's commercial viability is predicated on its ability to automate and standardize liquid biopsy analysis, reducing reliance on subjective interpretation and expanding access to personalized cancer screening.