Medical Information Processing Apparatus Model Update Strategy
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Solution Overview
Problem
Current medical decision support technologies, such as supervised learning, require large amounts of data and cannot demonstrate causal sequences, leading to inefficiencies and limitations in clinical decision-making, particularly in personalized medicine, and are hindered by the costs and time-consuming nature of randomized controlled trials.
Innovation Solution
A medical information processing apparatus and method that updates models for calculating effect evaluation values by separating parameter and structure updates, allowing for more frequent parameter updates while retaining the model structure, and adjusting the structure less frequently based on accumulated observation data, enabling adaptive and efficient medical decision refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the model structure is updated frequently to improve adaptability and refinement capability, then the degree of realizable refinement of medical decisions is improved, but the computational complexity and time consumption increase
Solution Approach 1:
The patent segments the model into two distinct components: parameters and structure. Parameters are updated frequently based on new observation data to capture current patterns, while the structure is updated less frequently to maintain stability. This segmentation allows independent optimization of each component's update frequency, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent implements dynamic update strategies where the update frequency of parameters and structure differs. Parameters undergo frequent updates to adapt to changing medical patterns, while structure updates are spaced out to avoid excessive complexity. This dynamic approach allows the system to balance adaptability with computational feasibility.
2Ease of operation
If the model structure is simplified to reduce computational complexity, then the ease of operation is improved, but the inference accuracy deteriorates
Solution Approach 1:
By segmenting the model into parameters and structure, the patent allows the structure to remain relatively simple and stable while parameters capture the necessary complexity for accurate inference. This segmentation enables the system to maintain computational efficiency through simple structure while achieving high accuracy through parameter optimization.
Solution Approach 2:
The patent relies on parameter changes rather than structural changes to achieve model adaptation. By updating parameters frequently based on observation data, the system maintains high inference accuracy without the computational burden of frequent structural modifications, thus preserving ease of operation.
3Measurement precision
If parameter updates are performed frequently to improve inference accuracy, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent segments update operations into two distinct processes: parameter updates (frequent) and structure updates (infrequent). This segmentation allows the system to manage update frequency by treating parameters and structure differently, reducing the complexity of update management while maintaining high inference accuracy through frequent parameter optimization.
4Adaptability or versatility
If the model structure is updated frequently to improve adaptability, then the refinement capability is improved, but the loss of time increases
Solution Approach 1:
The patent segments update operations into parameter updates (frequent, fast) and structure updates (infrequent, more time-consuming). This segmentation allows the system to achieve adaptability through frequent parameter updates without the time penalty of frequent structural modifications, thus reducing overall time loss while maintaining refinement capability.
Data Source
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AI summary
According to one embodiment, a medical information processing apparatus includes processing circuitry which updates a model for calculating an effect evaluation value for a medical decision. The processing circuitry updates a parameter of the model while retaining the structure of the model so that the structure of the model is updated less frequently than the parameter.