Personalized Insulin Therapy Using Digital Patient Twins
Solution Overview
Problem
Existing insulin delivery therapies for diabetes patients often fail to balance clinical outcomes with patient preferences, leading to suboptimal glucose level control due to insulin resistance or insufficient insulin production.
Innovation Solution
A machine-learning model is generated based on digital representations of multiple patients to correlate patient characteristics with insulin delivery therapies, predicting the best clinical outcome for an individual patient by balancing adherence and therapy effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine-learning model is generated based on digital representations of multiple patients to determine personalized insulin delivery therapy, then clinical outcome and therapy effectiveness are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent creates digital representations (digital twins) of multiple patients that replicate their physiological characteristics, insulin sensitivity, and glucose dynamics. These virtual copies are used to simulate and predict therapy outcomes without requiring complex real-time computational models during actual treatment, thereby improving reliability while managing complexity
Solution Approach 2:
The machine-learning model is pre-generated using digital representations of multiple patients before actual therapy determination. This preliminary model generation allows the system to quickly determine personalized insulin delivery therapies without requiring complex real-time computations, thus improving clinical outcomes while reducing operational complexity
2Reliability
If personalized insulin delivery therapy is determined using machine-learning models, then glucose level management is improved, but loss of time in therapy determination increases
Solution Approach 1:
The machine-learning model is pre-trained using digital representations of multiple patients with various characteristics and therapy regimens. This preliminary model generation allows for rapid therapy determination when actual patients present, as the system only needs to input patient characteristics and receive instant predictions from the pre-computed model, thus improving glucose level management while minimizing time loss
Solution Approach 2:
By creating digital twins of patients with known characteristics and outcomes, the system can quickly match and predict optimal therapies without performing complex real-time calculations. The digital representations enable fast lookup and interpolation of therapy recommendations based on similar patient profiles, reducing determination time while maintaining accuracy
Data Source
AI summary
Techniques for determining insulin therapy are described. The techniques include obtaining patient characteristic information for a current patient; determining, with a machine-learning model, an insulin delivery therapy from a plurality of insulin delivery therapies for the current patient based on the patient characteristic information, wherein the machine-learning model is generated based on digital representations of a plurality of patients; and outputting information indicative of the determined insulin delivery therapy.