<![CDATA[The increasing volume and heterogeneity of healthcare data have created challenges in identifying meaningful relationships among patients and translating complex clinical information into personalized healthcare decisions. Graph-based approaches provide a relational framework for representing patients and their clinical similarities, enabling patterns that may be difficult to identify through conventional analytical approaches to be examined systematically. This study aims to examine how graph-based similarity measures are used to represent patient relationships, identify clinically meaningful patterns, and support personalized healthcare. A qualitative secondary study was conducted by reviewing peer-reviewed literature published since 2018 and indexed in Scopus, Web of Science, and PubMed. Relevant studies were examined based on their relevance, clarity, and contribution to graph-based patient networks, similarity measures, and personalized healthcare, and the findings were synthesized thematically. The review identified four major contributions of graph-based approaches: patient clustering and pattern discovery, risk prediction and disease progression analysis, treatment selection and clinical decision support, and personalized patient monitoring. The literature also indicates that integrating heterogeneous data, including clinical histories, laboratory results, symptoms, treatment responses, imaging, and genetic information, can provide richer patient representations and strengthen similarity-based analyses. However, practical implementation remains constrained by data incompleteness and inconsistency, privacy and security requirements, limited interoperability, and the technical complexity of integrating multimodal healthcare data. Overall, graph-based similarity measures provide a promising framework for understanding patient variability and supporting data-driven personalized healthcare. Future research should prioritize empirical validation, longitudinal patient networks, explainable graph-based models, multimodal data integration, and privacy-preserving approaches to establish their clinical utility and reliability across diverse healthcare settings.]]>
