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.

病患中心知识图谱（PCKGs）是医疗保健领域的重要转变，通过以整体性和多维度的方式映射患者的健康信息，聚焦于个体化患者护理。这篇文献综述探讨了 PCKGs 的方法学、挑战和机遇，以及它们在整合不同医疗保健数据和通过统一的健康视角增强患者护理方面的作用。此外，本文还讨论了 PCKG 发展的复杂性，包括本体设计、数据整合技术、知识提取以及知识的结构化表示等。文章强调了推理、语义搜索和推理机制等先进技术在构建和评估可行的 PCKGs 时的重要性，并进一步探讨了 PCKGs 在个体化医学中的实际应用，强调了它们在改善疾病预测和制定有效治疗计划方面的重要性。总体而言，本文提供了关于 PCKGs 当前技术和最佳实践的基础视角，指导未来在这一动态领域的研究和应用。