Ontology-Based Parkinson’s Disease Monitoring and Alerting with PHKG-GNNs: Comparison
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Please note this is a comparison between Version 1 by Nikolaos Zafeiropoulos and Version 3 by Fanny Huang.

In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care was delved into, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This is anpaper, extension of researchers'ding our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, the is paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN) are focused on. T. This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications are emphasized. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, a nothe paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions is proposed.

  • ontology
  • knowledge graphs
  • Graph Neural Networks
  • Parkinson’s Disease
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