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REVIEW article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/frai.2024.1388479

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

Provisionally accepted
Hassan S. Al Khatib Hassan S. Al Khatib *Subash Neupane Subash Neupane Harish Kumar Manchukonda Harish Kumar Manchukonda Noorbakhsh A. Golilarz Noorbakhsh A. Golilarz Sudip Mittal Sudip Mittal Amin Amirlatifi Amin Amirlatifi Shahram Rahimi Shahram Rahimi
  • Mississippi State University, Starkville, United States

The final, formatted version of the article will be published soon.

    Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information holistically and multi-dimensionally. 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.

    Keywords: knowledge graph, Patient-centric, Personalized Healthcare, Natural Language Processing, Generative AI

    Received: 19 Feb 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Al Khatib, Neupane, Kumar Manchukonda, Golilarz, Mittal, Amirlatifi and Rahimi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hassan S. Al Khatib, Mississippi State University, Starkville, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.