Diagnosis Support Apparatus Using Bayesian Inference for Medical Data
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Solution Overview
Problem
Medical diagnosis support apparatuses face challenges in presenting relevant information to doctors, leading to low inference accuracy and potential diagnosis errors due to incomplete input data, as they often provide unnecessary or insufficient information when the expected diagnosis name differs from the estimated one.
Innovation Solution
A diagnosis support apparatus that acquires a user-set diagnosis name and provides negative information based on the input information, using a Bayesian network for inference and selecting support information candidates to present relevant data, thereby enhancing the reliability of the diagnosis by highlighting influential non-input information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the medical diagnosis support apparatus presents reference information without explanation, then the information presentation is simple and quick, but the doctor cannot determine the reliability of the reference information
Solution Approach 1:
The patent introduces an explanation information generation unit that acts as an intermediary between the inference result and the doctor. This unit generates explanation information that describes the relationship between input information and inference results, allowing doctors to assess reliability without complicating the interface. The explanation serves as a mediator that bridges the gap between simple presentation and reliability assessment.
2Loss of information
If the apparatus presents all non-input information to the doctor, then complete information is provided, but the information becomes excessive and difficult to process
Solution Approach 1:
The patent applies local quality by selectively presenting different types of explanation information based on the specific diagnostic context. Rather than uniformly presenting all non-input information, the system generates explanations tailored to the relationship between specific input information and inference results, providing locally optimized information quality where it is most needed.
Solution Approach 2:
The explanation information is segmented into distinct components that correspond to specific relationships between input information and inference results. This segmentation allows doctors to process information in manageable units rather than as a single large block, reducing cognitive load while maintaining completeness.
3Reliability
If the apparatus calculates and presents explanation information for all inference results, then complete diagnostic support is provided, but the calculation time and processing load increase significantly
Solution Approach 1:
The patent implements partial action by generating explanation information selectively rather than exhaustively for all possible inference results. The system focuses on generating explanations for the most relevant inference results and relationships, providing sufficient diagnostic support without the excessive computational burden of complete analysis.
Data Source
AI summary
A diagnosis support apparatus which supports diagnosis based on information associated with a diagnosis name in advance is provided. In the diagnosis support apparatus, an acquisition unit acquires the diagnosis name set by a user. A providing unit provides negative information for the diagnosis name set by the user based on the information. With the above arrangement, the diagnosis support apparatus selects and presents information influencing the diagnosis name expected by the user, thereby efficiently presenting information required by the user.