Medical Diagnosis Support Inference Model Selection
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Solution Overview
Problem
Existing medical diagnosis support systems face challenges in constructing inference models that balance performance and the validity of presented information, often prioritizing one over the other, leading to difficulties in maintaining accurate and informative diagnoses over time.
Innovation Solution
An information processing apparatus and method that evaluates both inference performance and information validity using multiple units to select and update inference models, ensuring they remain effective and informative throughout operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the inference means is constructed focusing only on performance using machine-based learning, then the inference performance is improved, but the capability to display valid information deteriorates
Solution Approach 1:
The evaluation process is segmented into two distinct evaluation units: one evaluating inference performance and another evaluating information validity. This allows independent assessment of both metrics without one compromising the other, enabling selection of inference means that balance both requirements
Solution Approach 2:
The system changes the evaluation parameters by introducing a dual-criteria evaluation framework. Instead of using a single performance metric, it simultaneously evaluates inference accuracy and information validity, allowing selection of inference means that optimize both parameters rather than sacrificing one for the other
2Adaptability or versatility
If the inference means is updated periodically using additional data, then the adaptability to changing data conditions is improved, but the complexity of constructing and maintaining the inference means increases
Solution Approach 1:
The system implements periodic updates of the inference means using additional data collected during operation. By updating at regular intervals rather than continuously, it achieves adaptability to changing data conditions while controlling the complexity and computational burden of model reconstruction
Solution Approach 2:
The system uses feedback from dual evaluation of inference performance and information validity to guide periodic updates. The feedback mechanism identifies when updates are necessary and what adjustments should be made, reducing the complexity of model maintenance by providing clear directional guidance for updates
Data Source
AI summary
A medical diagnosis support apparatus includes a training data obtaining unit that obtains training data, an inference means candidate creating unit that creates a plurality of inference means candidates based on the training data, an inference performance evaluation unit that evaluates the performance of the plurality of inference means candidates based on the training data, an information validity evaluation unit that evaluates the validity of information presented by each of the plurality of inference means candidates based on the training data, and an inference means selection unit that selects an inference means from the plurality of inference means candidates based on the performance of the plurality of inference means candidates and the validity of the information presented by each of the plurality of inference means candidates.