Adaptive Diagnostic Model for Biological State Analysis
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Solution Overview
Problem
Existing diagnostic models are static and assume that the sample set used to develop them is representative of the population, leading to questionable validity when new, unknown biological samples are analyzed, and there is a need for monitoring their applicability and updating to reflect changes in the population.
Innovation Solution
A method is developed to build, deploy, and update diagnostic models using a centralized bioinformatics system, where biological samples are analyzed at one location and data is transmitted to another for analysis, allowing for the assessment of model validity and updating based on new samples, using techniques like the Knowledge Discovery Engine (KDE) to identify discriminatory patterns and recalculate cluster centroids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static diagnostic model is built from a finite sample set and used to assess new biological samples, then the model development is simple and quick, but the model's validity and reliability deteriorate when the sample population changes or drifts occur
Solution Approach 1:
The patent transforms the static diagnostic model into a dynamic system that continuously adapts to new biological samples. The model evolves by incorporating new samples through iterative training cycles, allowing it to track changes in the underlying population distribution over time. This dynamic approach resolves the contradiction by making the model reliable for changing populations while maintaining reasonable development throughput through automated processes.
Solution Approach 2:
The patent implements a feedback mechanism where the performance of the diagnostic model is continuously monitored using new biological samples. When performance degradation is detected (indicating population drift), the system automatically triggers model retraining with accumulated samples. This feedback loop ensures model validity is maintained without requiring manual intervention, resolving the contradiction between quick development and ongoing reliability.
2Device complexity
If all biological sample data are analyzed at the same location where samples are collected, then data processing is simple, but system adaptability and model updating capability are limited
Solution Approach 1:
The patent divides the diagnostic system into separate functional modules: sample collection sites, centralized data processing centers, and model training facilities. This segmentation allows data to be collected at multiple distributed locations and processed at centralized facilities with greater computational resources. The modular architecture enables model updates to be performed centrally and distributed back to all collection sites, resolving the contradiction between simple architecture and adaptability.
Solution Approach 2:
The patent introduces a centralized bioinformatics processing center as an intermediary between sample collection sites and model deployment. This intermediary collects data from multiple sources, performs comprehensive analysis, and distributes updated models back to the network. This intermediary structure enables sophisticated model updating capabilities while keeping individual collection sites relatively simple, resolving the architectural contradiction.
3Measurement precision
If a diagnostic model is updated frequently to reflect new samples, then model accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent implements periodic model updating rather than continuous updates. New biological samples are accumulated over time, and the model is retrained at scheduled intervals or when performance thresholds are breached. This periodic approach maintains high diagnostic accuracy by incorporating new population characteristics while avoiding the time and computational overhead of continuous retraining, resolving the contradiction between accuracy and update time.
Solution Approach 2:
The patent changes the parameter of update frequency from continuous to periodic/threshold-based. Instead of updating the model with every new sample, the system monitors performance metrics and only triggers retraining when accuracy degradation exceeds a threshold or at predetermined intervals. This parameter change maintains diagnostic precision while significantly reducing the time and computational resources required for model updates.
Data Source
AI summary
A model of a particular biological state can be developed. The model may be used to determine if an unknown biological sample exhibits a particular biological state. This can be done by receiving either a biological sample or data associated with the biological sample. After the data is received, the data may be input into the model. In one embodiment, the acquisition of the data associated with the biological sample is performed at a first location and the imputing of the data into the model is performed at a second location different than the first location. Unless the data maps identically to the model, the data would have an inherent effect on the position of the particular clusters within the discriminatory pattern, if it is allowed to affect the model. The modeling software can keep track of the net effect on the model that each sample received has on the position of the model. If the model has drifted outside of a predetermined tolerance, the model can be updated. Various business relationships may be developed to undertake various steps of the overall method for providing a diagnosis to a patient.