Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications

TL;DR

Patient-Centric Knowledge Graphs (PCKGs) address the problem of fragmented, heterogeneous health data by embedding patient information into interconnected knowledge graphs. The paper surveys current methods, presents a four-part taxonomy (Construction, Evaluation, Processing, Applications), and details techniques in ontology design, data sourcing, information extraction, and reasoning. It also discusses practical applications in disease prediction, personalized interventions, and clinical trials, along with challenges such as data quality, privacy, and interoperability. The findings suggest that combining ontologies, multi-source data integration, and advanced reasoning over PCKGs can enhance personalized medicine and clinical decision support.

Abstract

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.

Figures (5)

• Figure 1: An example of knowledge graph, where the triplet ($v_9$, $r_1$, $v_8$) serves as an illustration of the link between entities $v_9$ and $v_8$ through the relation $r_1$ and ($v_8$, $r_2$, $v_1$) through $r_2$ for relation between $v_8$ and $v_1$.
• Figure 2: Illustration of Patient's Clincal Visits Knowledge Graph
• Figure 3: Proposed Taxonomy of Patient-Centric Knowledge Graph
• Figure 4: Illustration of a Basic Patient's Ontology
• Figure 5: Diverse Sources of Medical and Healthcare Data