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

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

URL: http://arxiv.org/abs/2402.12608v1
Date: Tue, 20 Feb 2024 00:07:55 GMT
Title: Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications
Authors: Hassan S. Al Khatib, Subash Neupane, Harish Kumar Manchukonda, Noorbakhsh Amiri Golilarz, Sudip Mittal, Amin Amirlatifi, Shahram Rahimi
Abstract summary: Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs.
Score: 2.913761513290171
License: http://creativecommons.org/licenses/by/4.0/
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.

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