Healthcare providers face significant challenges with monitoring and managing patient data outside of clinics, particularly with insufficient resources and limited 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, clinician interpretation and querying of patient data. Through a use case of monitoring Chronic Obstructive Pulmonary 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.

翻译：医疗提供者在院外监测和管理患者数据方面面临重大挑战，尤其在资源不足且对患者病情反馈有限的情况下。有效管理这些症状并探索更大规模的数据集对于维持长期生活质量、防止延迟干预至关重要。本文提出了一种从异构数据源构建个人健康知识图谱的框架。该方法整合临床数据库、相关本体及标准医疗指南，以支持警报生成、临床医生解读及患者数据查询。通过慢性阻塞性肺疾病（COPD）患者监测的用例，我们验证了基于本框架构建的个人健康知识图谱的推理与推断能力，可辅助患者监测并提升患者数据查询的效率与准确性。