Interlinking Clinical Guidelines via Mining Medical Literature Knowledge for Multi-Morbidity Decision-Making

Independently developed clinical guidelines present a systematic challenge in managing patients with multi-morbidity in a consistent and integrated manner. Existing approaches mainly focus on combining multiple guidelines and lack approaches that combine with additional medical resources. The correlations and conflicts between treatment plans in the management of multi-morbidity are well-documented in medical literature but are less explored in the Clinical Decision Support line of research. In this paper, we propose a literature-based guideline interlinking method to address these challenges through the integration of clinical guidelines and the harmonization of conflicting recommendations, thereby providing a more holistic and efficient way to manage patients with multi-morbidity conditions. This method employs an ontology model and knowledge graph technology to represent and analyze the complexity and interrelations of diseases, with the aim of transcending the limitations of traditional single disease guidelines and providing a holistic and integrated framework for multi-morbidity management. The objective is to construct a multi-morbidity knowledge graph by correlating medical literature with clinical guidelines and to provide optimal decision support for patients with multi-morbidity complications in a clinical decision support system (CDSS).