Dynamic Classifier Selection for Medical Diagnosis
Solution Overview
Problem
Existing information processing apparatuses for medical diagnosis lack the ability to dynamically set classifiers that are tailored to the specific needs and requirements of individual medical institutions, leading to suboptimal detection performance for medical treatment data.
Innovation Solution
An information processing apparatus that includes a classifier and a hardware processor capable of setting the classifier's properties, evaluating the sufficiency of these properties using evaluation data, and selecting the most appropriate classifier for a specific medical institution based on the evaluation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single classifier is applied to all medical institutions, then the device complexity is reduced, but the detection precision and adaptability to specific medical institution requirements deteriorate
Solution Approach 1:
The system dynamically selects and configures classifiers based on the specific requirements of each medical institution. Instead of using a static single classifier for all institutions, the system adapts the classifier configuration to match the detection performance requirements, detection targets, and operational characteristics of individual medical institutions, thereby resolving the contradiction between simplicity and precision.
Solution Approach 2:
The system changes the parameters of the classifier (such as detection targets, detection performance metrics like sensitivity and specificity, and operational modes) according to the specific needs of different medical institutions. This allows the same base classifier to be customized for different institutions without increasing overall system complexity, as the changes are managed through parameter configuration rather than structural modification.
2Adaptability or versatility
If multiple classifiers are developed to meet different medical institution requirements, then the detection precision and adaptability improve, but the device complexity and difficulty of selection increase
Solution Approach 1:
The system incorporates an evaluation mechanism that assesses the performance of different classifiers based on medical institution requirements. This feedback loop allows the system to automatically determine which classifier is most suitable for a given institution, reducing the complexity of manual selection and management while maintaining high adaptability. The evaluation results guide the automatic selection process, making the system intelligent and self-regulating.
3Measurement precision
If the classifier is customized for each medical institution, then the detection precision meets specific requirements, but the ease of operation and implementation difficulty increase
Solution Approach 1:
The system enables medical institutions to self-configure their optimal classifier through an automated evaluation and selection process. Instead of requiring complex manual customization for each institution, the system allows institutions to input their basic requirements, and the system automatically evaluates and selects the most appropriate classifier configuration. This self-service approach maintains high detection precision while significantly reducing the operational burden and implementation difficulty.
Data Source
AI summary
An information processing apparatus including: a classifier that is applied for medical treatment data; and a hardware processor that sets a property of the classifier, evaluates a sufficiency level of the property by using evaluation data for the classifier, and selects and sets one classifier as the classifier to be used in a facility by referring to an evaluation result.