Insulin Pump Settings via Unsupervised Learning
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Solution Overview
Problem
Conventional insulin pumps require significant physician expertise and patient cooperation to set and adjust insulin pump settings, which can be sensitive to patient cooperation and require manual resets, leading to suboptimal glucose control.
Innovation Solution
A monitoring system using unsupervised learning to analyze raw log data from glucose monitoring devices, isolating informative data and calculating optimal global insulin pump settings independently of patient cooperation, including basal rate, correction factor, carbohydrate ratio, and insulin activity curve, without human interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional insulin pumps use manual physician setting adjustment, then physician expertise can be applied to optimize treatment, but the process requires significant time and patient cooperation which reduces reliability
Solution Approach 1:
The insulin pump system performs self-adjustment of settings by automatically analyzing its own operational data and patient response data to optimize insulin delivery parameters without requiring continuous physician intervention. The system serves itself by extracting insights from accumulated data and autonomously adjusting basal rates, bolus amounts, and timing based on observed glucose control patterns.
Solution Approach 2:
The system implements continuous feedback loops where patient glucose monitoring data and insulin delivery history are systematically analyzed to inform subsequent setting adjustments. The feedback mechanism processes historical data patterns to automatically refine insulin pump settings, creating a closed-loop control system that improves reliability over time through iterative optimization.
2Adaptability or versatility
If insulin pump settings are manually adjusted by physicians, then personalized treatment can be achieved, but patient cooperation is required which introduces sensitivity and potential errors
Solution Approach 1:
The system automatically generates personalized treatment settings by analyzing its own operational data and patient response patterns without requiring manual physician interpretation. The autonomous system extracts individualized insulin sensitivity, carbohydrate ratio, and correction factor settings directly from accumulated data, eliminating variability introduced by manual assessment while maintaining personalization.
Solution Approach 2:
The system performs preliminary analysis of patient data patterns and pre-calculates optimized insulin pump settings before clinical visits or adjustments are needed. By continuously processing data in the background and preparing optimized settings in advance, the system ensures consistent, data-driven personalization is ready for immediate implementation without requiring time-consuming manual analysis.
3Stability of the object's composition
If global pump settings remain constant until manual reset, then system stability is maintained, but the settings become suboptimal over time as patient needs change
Solution Approach 1:
The system transitions from static constant settings to dynamic adaptive settings that automatically adjust based on real-time analysis of patient response data. The insulin pump settings become dynamic variables that evolve with patient needs, allowing basal rates, bolus amounts, and timing parameters to change automatically in response to observed glucose control patterns and lifestyle variations.
Solution Approach 2:
The system maintains continuous optimization of insulin pump settings through uninterrupted analysis of operational data and patient responses. Rather than discrete manual adjustments, the system performs continuous useful action by constantly processing data streams and maintaining optimized settings, ensuring glucose control effectiveness is continuously improved without interruption or manual intervention gaps.
Data Source
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AI summary
A monitoring system and method is presented for use with diabetic treatment management. The monitoring system comprises a control unit, and a communication interface, which configured and operable to permit access to stored raw log data, which have been obtained over a certain time and being time spaced data points of glucose measurements, meals consumed and insulin delivery. The control unit comprises an unsupervised learning controller configured and operable to receive and process said raw log data, to determine an informative data piece from residual log data portion of said raw log data and select said informative data piece for retrospective analysis to calculate at least one global insulin pump setting of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters.