Disease Treatment Simulation With Time Attenuation
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Solution Overview
Problem
Current disease treatment simulation models for chronic conditions like type 2 diabetes mellitus lack dynamic feedback and fail to effectively represent the long-term effects of physician decisions, as well as focus on the clinical physician-patient encounter, which is crucial for improving physician decision-making.
Innovation Solution
A method and system that simulate patient states through a series of virtual physician-patient encounters, calculating the maximal expected incremental effect of treatment moves over time, incorporating a user interface, encounter state loader, integrator, time attenuator, and updater to generate an updated patient state, allowing for dynamic simulation and projection of treatment outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing disease treatment simulation models are used, then the basic disease progression can be modeled, but dynamic feedback and long-term effects of physician decisions cannot be represented
Solution Approach 1:
The simulation system segments the treatment effect calculation into distinct functional modules: an integrator that accumulates treatment effects over time, a time-attenuator that models decay of treatment effects, and an encounter-state-loader that manages patient state data. This modular segmentation enables complex dynamic feedback while maintaining manageable system complexity through organized component separation.
Solution Approach 2:
The system implements dynamic feedback by using the integrator to accumulate treatment effects from previous encounters and feed them back into the current patient state. The time-attenuator modifies this feedback based on elapsed time, creating a realistic dynamic feedback loop that represents how treatment effects persist and diminish over time in chronic disease management.
2Measurement precision
If treatment effects are calculated without time attenuation, then the maximal effect is captured, but the gradual diminishment of treatment effects over time is not represented
Solution Approach 1:
The time-attenuator dynamically changes the treatment effect parameter based on elapsed time since the previous encounter. As time increases, the attenuation factor modifies the treatment effect magnitude to reflect clinical reality where treatment effects diminish or plateau over time. This parameter change approach maintains measurement precision while adding realistic temporal dynamics to the calculation.
3Loss of time
If the simulation focuses only on individual encounters, then immediate treatment effects are captured, but long-term cumulative effects are lost
Solution Approach 1:
The integrator performs preliminary accumulation of treatment effects from previous encounters before the current encounter is processed. By pre-calculating and storing the cumulative treatment effect in the encounter state, the system preserves long-term information without requiring complex recalculation during each new encounter, thus preventing information loss while extending the simulated time span.
Data Source
AI summary
There are provided a method, system and medium having instructions embedded to determine an attribute of a simulated patient's state resulting from a patient-physician encounter. The method includes receiving a current treatment move and an initial value of the attribute; calculating a maximal expected incremental effect value of the current treatment move over a previous treatment move on the attribute; attenuating the maximal expected incremental effect value based on amount of time between the previous treatment move and the current treatment move; summing the attenuated maximal expected incremental effect value with the initial value to generate an updated attribute of the simulated patient's state; and generating a patient-physician encounter that includes the updated attribute.