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

arXiv: https://arxiv.org/abs/2402.12608 · HTML: https://mlsanity.com/paper/2402.12608/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 158 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [Product Knowledge Graph Embedding for E-commerce](https://mlsanity.com/paper/1911.12481/) (2019)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2004.11861/) (2020)
- [Neuro-symbolic representation learning on biological knowledge graphs](https://mlsanity.com/paper/1612.04256/) (2016)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)
- [Multimodal Learning on Graphs for Disease Relation Extraction](https://mlsanity.com/paper/2203.08893/) (2022)
- [Language Models with Rationality](https://mlsanity.com/paper/2305.14250/) (2023)
- [Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph](https://mlsanity.com/paper/1910.01116/) (2019)
- [LinkNBed: Multi-Graph Representation Learning with Entity Linkage](https://mlsanity.com/paper/1807.08447/) (2018)
- [Personal Health Knowledge Graphs for Patients](https://mlsanity.com/paper/2004.00071/) (2020)
- [Applying Personal Knowledge Graphs to Health](https://mlsanity.com/paper/2104.07587/) (2021)
- [Improving Question Answering over Knowledge Graphs Using Graph Summarization](https://mlsanity.com/paper/2203.13570/) (2022)
- [A review of knowledge graph application scenarios in cyber security](https://mlsanity.com/paper/2204.04769/) (2022)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition](https://mlsanity.com/paper/1804.05017/) (2018)
- [Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph](https://mlsanity.com/paper/2204.13329/) (2022)
- [Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes](https://mlsanity.com/paper/2005.07654/) (2020)
- [Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2010.08300/) (2020)

## Built on by (most cited first, 6 of 6)

- [An Auditable Agent Platform For Automated Molecular Optimisation](https://mlsanity.com/paper/2508.03444/) (2025)
- [Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2511.13526/) (2025)
- [Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering](https://mlsanity.com/paper/2601.09720/) (2025)
- [From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction](https://mlsanity.com/paper/2503.16533/) (2025)
- [VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs](https://mlsanity.com/paper/2604.23356/) (2026)
- [Personal Care Utility: Health as Everyday Infrastructure](https://mlsanity.com/paper/2606.14145/) (2026)

## Abstract (arXiv)

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.