Accurate nutritional assessment is critical for public health, but existing profiling systems require detailed data often unavailable or inaccessible from colloquial text descriptions of food. This paper presents a machine learning pipeline that predicts the comprehensive Food Compass Score 2.0 (FCS) from text descriptions. Our approach uses multi-headed neural networks to process hybrid feature vectors combining semantic embeddings, lexical features, and domain-specific heuristics.
10 pages, 4 figures, 6 tables, submitted to AMIA 2026 Informatics Summit