Blood-glucose pattern mining algorithm for decision support in diabetes management

Pattern recognition has been an effective approach to identifying glycaemic patterns within self-monitored blood glucose (BG) data in diabetes mellitus patients. This paper presents a new BG pattern mining algorithm for more targeted therapeutic decision support in diabetes self-management. Based on patients' BG readings which are collected via a handheld device and logged on a web-based health portal, the existing BG patterns are extracted in real-time and fed back to the patient along with appropriate therapeutic recommendations, educational modules and health care advice. The identified patterns help patients improve their blood glucose management and education about diabetes and its complications. A functional prototype of the proposed system is developed and its end-to-end functionality is successfully demonstrated. A pilot clinical study demonstrated positive user acceptability and interest in its decision support attributes for diabetes self-management, making this a promising avenue for further research.