Explainable Clinical Pathway Analysis Apparatus
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Solution Overview
Problem
Current machine learning models, particularly deep learning methods, lack interpretability, making it difficult to understand the relevance between prediction results and clinical pathways in medical diagnosis or treatment, which hinders their integration into clinical practices.
Innovation Solution
An analysis apparatus that generates explainable input data using a neural network to output prediction results along with the importance of feature items, allowing for the editing and visualization of clinical pathways, thereby improving interpretability and facilitating the introduction of machine learning into medical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models (particularly deep learning) are used for prediction, then prediction accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent introduces an intermediary system that connects the machine learning model and the clinical pathway. This intermediary translates the model's prediction results into clinically interpretable formats by mapping predictions to relevant clinical pathways and generating explanations that bridge the gap between complex model outputs and clinical decision-making contexts
Solution Approach 2:
The patent replaces the traditional mechanical explanation approach (direct model output interpretation) with an information-processing system that automatically generates explanations. This substitution transforms the interpretability problem from a manual analysis task into an automated information generation process that produces clinically relevant explanations
2Reliability
If machine learning models are introduced into clinical practices, then diagnostic and treatment support is improved, but integration difficulty increases
Solution Approach 1:
The patent creates a universal interface system that can work with multiple types of machine learning models and clinical pathways simultaneously. This multi-functional system handles various prediction tasks (diagnosis, treatment recommendations, prognosis) through a unified explanation generation mechanism, reducing integration complexity across different clinical applications
Solution Approach 2:
The intermediary system serves as a universal adapter between diverse machine learning models and clinical workflows. It standardizes the interaction interface by translating different model outputs into a common clinically interpretable format, thereby simplifying integration across various clinical practices
3Measurement precision
If clinical pathways are updated with actual clinical data, then clinical pathway accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where machine learning prediction results and their explanations are used to automatically update and refine clinical pathways. The system continuously learns from actual clinical data, using the generated explanations to identify patterns and improvements in clinical pathways without requiring complex manual data processing
Solution Approach 2:
The system performs self-updating of clinical pathways by automatically processing clinical data through the explanation generation mechanism. The explanation system itself serves the dual purpose of providing interpretability and simultaneously updating the clinical pathways with newly discovered patterns from actual data, reducing external processing requirements
Data Source
AI summary
An analysis apparatus comprises: a generation module configured to generate a second piece of input data having a weight for a first feature item of a patient based on: a first piece of input data relating to the first feature item; a second feature item relating to a transition to a prediction target in a clinical pathway relating to a process for diagnosis or treatment; and a clinical terminology indicating relevance between medical terms; a neural network configured to output, when being supplied with the first piece of input data and the second piece of input data generated, a prediction result for the prediction target in the clinical pathway and importance of the first feature item; an edit module configured to edit the clinical pathway based on the prediction result and the importance output from the neural network; and an output module configured to output an edit result.