Discrete Patient Representations for Explainable Downstream Predictions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current explainable models require access to the full AI model, making feature extraction and prediction tasks inseparable, which hinders the explainability of downstream tasks in patient representation learning.
Innovation Solution
A method that generates invariant feature representations of patients, allowing for separate feature extraction and prediction tasks, and provides explanations associated with discrete features for improved explainability in healthcare systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic patient representations are generated using graph neural networks to improve generalizability, then the system can handle multiple downstream tasks, but explainability of the downstream tasks is hindered
Solution Approach 1:
The system segments the patient representation into discrete, interpretable features that can be independently explained. Instead of using a monolithic generic representation, the patent divides the patient data into distinct feature components that maintain generalizability while enabling task-specific explanations for different downstream applications.
Solution Approach 2:
The patent introduces an intermediary layer between the graph neural network representation and the downstream tasks. This intermediary structure provides discrete features that serve as a bridge, preserving the generalizability of the neural network while making the information accessible and explainable for various downstream applications without requiring access to the full AI model.
2Loss of information
If current explainable models require access to the full AI model to provide explanations, then explanations can be generated, but feature extraction and prediction tasks become inseparable
Solution Approach 1:
The patent extracts the essential explanatory information from the complex AI model by generating discrete patient features that can be used independently. These extracted features capture the necessary information for explanations without requiring access to the full AI model, thereby separating the explanation generation from the complex model infrastructure.
Solution Approach 2:
The system creates a simplified copy of the patient representation in the form of discrete features that replicate the essential information needed for explanations. This copied representation maintains the explanatory power of the full model while being independently accessible and usable without the complex AI model infrastructure.
3Adaptability or versatility
If feature extraction and prediction tasks are separated to improve system modularity, then system flexibility increases, but explainability becomes impossible
Solution Approach 1:
The discrete patient features serve multiple functions simultaneously: they enable modular feature extraction, support various prediction tasks, and provide explainability. This universal feature representation maintains system modularity while ensuring that explanations remain possible across different downstream applications.
Data Source
AI summary
A method for improving explainability of patient representations includes generating one or more patient representations of a patient based on building one or more invariant feature representations of the patient. The one or more patient representations indicate one or more discrete features. The method further includes determining predictions for one or more downstream tasks based on using the one or more discrete features and providing explanations associated with the one or more discrete features. The explanations are associated with the predictions for the one or more downstream tasks.