Centro Investigación Gestión e Ingeniería Producción. Universitat Politècnica de València, Spain.
ITACA. Universitat Politècnica de València, Spain.
Stud Health Technol Inform. 2024 Aug 22;316:1699-1703. doi: 10.3233/SHTI240750.
Effective management of diabetes necessitates efficient data handling, insightful analytics, and personalized interventions. In this study, we present a comprehensive system that automates the extraction, transformation, and loading of continuous glucose monitoring data. Data is integrated into an interactive dashboard with dual access levels: one for healthcare management professionals and another for patients for clinical management. The dashboard provides real-time updates and customizable visualization options, empowering users with actionable insights into their glucose levels. Furthermore, a clustering model to categorize patients into distinct groups based on their glucose profiles was developed. Through this model, three clusters representing different patterns of glucose control are identified. Healthcare professionals can utilize these insights to tailor treatment strategies, allocate resources effectively, and identify high-risk patients.
糖尿病的有效管理需要高效的数据处理、深入的分析和个性化干预。在本研究中，我们提出了一个全面的系统，该系统可自动提取、转换和加载连续血糖监测数据。数据集成到一个具有双重访问级别的交互式仪表板中：一个供医疗保健管理专业人员使用，另一个供患者进行临床管理使用。该仪表板提供实时更新和可定制的可视化选项，使用户能够获得有关其血糖水平的可操作见解。此外，还开发了一个聚类模型，根据患者的血糖谱将其分为不同的组。通过该模型，确定了三个代表不同血糖控制模式的聚类。医疗保健专业人员可以利用这些见解来定制治疗策略、有效分配资源和识别高风险患者。
