Healthcare providers face significant challenges with managing and monitoring patient data outside of clinics, particularly with limited resources and insufficient feedback on their patients' conditions. Effective management of these symptoms and exploration of larger bodies of data are vital for maintaining long-term quality of life and preventing late interventions. In this paper, we propose a framework for constructing personal health knowledge graphs from heterogeneous data sources. Our approach integrates clinical databases, relevant ontologies, and standard healthcare guidelines to support alert generation, clinicians' interpretation and querying of patient data. Through a use case focusing on monitoring Chronic Obstructive Lung Disease (COPD) patients, we demonstrate that inference and reasoning on personal health knowledge graphs built with our framework can aid in patient monitoring and enhance the efficacy and accuracy of patient data queries.
|Nifer y tudalennau
|Cyhoeddwyd - Medi 2023
|CONFERENCE ON COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS & BIOSTATISTICS - Padova, Yr Eidal
Hyd: 06 Medi 2023 → 08 Medi 2023
Rhif y gynhadledd: 18th
|CONFERENCE ON COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS & BIOSTATISTICS
|06 Medi 2023 → 08 Medi 2023