1. A continuous glucose monitoring system comprising a wearable biosensor, an AI powered processing unit, a nurse-accessible dashboard, and a dose recommendation engine, wherein the AI engine processes real-time and historical glucose data to suggest optimal insulin dosages.

2. The system of claim 1, wherein the AI engine is trained using supervised learning models including patient-specific features and clinical guidelines.

3. The system of claim 1, further comprising a cloud-based storage module for archiving glucose readings, insulin dosages, and nurse interventions.

4. The system of claim 1, wherein the nurse dashboard provides real-time alerts, allows dose override, and supports remote monitoring.

Description:Diabetes mellitus is a chronic metabolic disorder that requires precise monitoring
and insulin management. Traditional methods of insulin administration often
depend on manual blood glucose monitoring and physician intervention. These
processes can delay treatment adjustments and are prone to human error. Nurses
are increasingly playing a vital role in managing diabetic patients, particularly in
hospital and home care settings. There is a growing need for an intelligent system
that continuously monitors glucose levels and provides real-time, nurse-assisted
insulin dose recommendations. , Claims:1. A continuous glucose monitoring system comprising a wearable biosensor, an AI
powered processing unit, a nurse-accessible dashboard, and a dose
recommendation engine, wherein the AI engine processes real-time and historical
glucose data to suggest optimal insulin dosages.
2. The system of claim 1, wherein the AI engine is trained using supervised learning
models including patient-specific features and clinical guidelines.
3. The system of claim 1, further comprising a cloud-based storage module for
archiving glucose readings, insulin dosages, and nurse interventions.
4. The system of claim 1, wherein the nurse dashboard provides real-time alerts,
allows dose override, and supports remote monitoring.