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About this paper
Hassan S. Al Khatib, Subash Neupane, Harish Kumar Manchukonda, Noorbakhsh Amiri Golilarz, Sudip Mittal, Amin Amirlatifi, Shahram Rahimi
The field of Patient-Centric Knowledge Graphs (PCKGs) receives a structured overview through a survey that introduces a novel taxonomy for their construction, evaluation, processing, and applications. This systematic review clarifies how PCKGs integrate diverse healthcare data to enable personalized medicine, proactive disease prediction, and enhanced clinical trials, alongside outlining persistent challenges in data quality and scalability.
Key insights
- Patient-Centric Knowledge Graphs are essential for enabling personalized medicine by integrating a holistic view of individual patient data.
- Sophisticated knowledge extraction techniques (e.g., deep learning for NER and RE) are crucial for building robust PCKGs from heterogeneous data sources.
- Despite their potential, PCKGs face critical challenges in data quality, scalability, privacy, and seamless integration into clinical workflows.
Summary by alphaXiv.
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