Knowledge Graph–Guided Domain Generalization for Computational Phenotyping: A Tutorial

Computational phenotyping increasingly requires integrating heterogeneous biomedical data across electronic health records (EHRs), registries, imaging, and multi-omics. Deeply phenotyped research cohorts provide disease-specific assessments and high-resolution modalities, but are limited in scale, whereas real-world EHRs are larger and more diverse but often lack specialized outcomes and expensive modalities. This mismatch creates a barrier to transferring research-grade signals into routine clinical data.This tutorial presents a knowledge graph (KG)–guided framework for cross-data generalization of phenotype representations under multimodal heterogeneity. We introduce a hybrid patient– knowledge graph architecture that anchors learned representations to shared biomedical entities and relations, and we discuss label-free domain adaptation strategies for target settings where disease-specific labels are unavailable. Through multi-modal disease prediction examples, we illustrate how research-grade signals can be translated into heterogeneous real-world data while preserving interpretability and predictive utility.