Integrating clinical evidence for multi-condition care

Clinical guidelines typically focus on single diseases and often overlook the complex comorbidities emerging in the aftermath of COVID-19. As growing evidence links COVID-19 to long-term neurological outcomes, integrative approaches are required to uncover cross-disease relationships. We present a graph-based framework that combines clinical trial data, biomedical mechanisms, and guideline recommendations to support comorbidity-aware care. Our approach comprises two components: (1) a semantic analysis of clinical guidelines for COVID-19, Parkinson’s disease, Alzheimer’s disease, and Multiple Sclerosis (MS) using MPNet-based sentence embeddings and UMAP; and (2) the construction of a comorbidity knowledge graph (KG) in Neo4j that integrates clinical trials, mechanistic hypotheses, and guideline content enriched with ontological annotations. Semantic clustering reveals distinct disease-specific patterns; however, when condition names are masked, neurological disorders begin to overlap while COVID-19 remains isolated, exposing thematic gaps in current guidance. The KG further identifies intermediates such as stroke and olfaction disorders that link COVID-19 to neurodegenerative diseases, highlighting under-recognized comorbidities. This study lays the groundwork for AI-driven, comorbidity-aware clinical decision support. By aligning clinical and mechanistic evidence, it enables adaptive and explainable tools, such as Retrieval-Augmented Generation, to assist clinicians and policy-makers in navigating complex multi-condition scenarios.