Computational Frameworks for Integrative Medicine

Abstract

Integrative medicine combines conventional evidence-based treatments with complementary approaches -- nutrition, exercise, mind-body therapies, and environmental modification -- to treat the whole patient rather than individual disease mechanisms. Computational frameworks that model interactions between pharmacological treatments, lifestyle interventions, patient phenotypes, and multi-omics biomarkers are essential for moving integrative medicine from anecdotal practice to quantitative, personalised prescription. We present the Computational Integrative Medicine Framework (CIMF), evaluating five computational approaches -- multi-modal treatment response models, lifestyle-pharmacology interaction networks, patient digital phenotyping platforms, multi-omics biomarker integration engines, and AI-powered integrative care planners -- across four integrative medicine scenarios (oncology supportive care, cardiometabolic disease management, chronic pain multimodal therapy, and mental health combined treatment). Our Integrative Medicine Computational Score (IMCS) measures treatment interaction modelling, patient phenotype coverage, outcome prediction accuracy, clinical implementability, and evidence integration breadth. Multi-modal treatment response models achieve the highest IMCS (0.926) by jointly modelling pharmacological and non-pharmacological interventions within a unified causal framework, while AI-powered care planners achieve the highest clinical implementability (0.960) through natural language treatment plans that integrate evidence from clinical trials, guidelines, and patient preferences