Supported Decision Trees for Transparent Medical Diagnosis
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Solution Overview
Problem
Current automated systems for providing medical advice and diagnosis lack transparency and interpretability, relying on complex algorithms that are difficult to understand and require extensive expertise, making them less effective in resource-constrained settings and less accessible for general practitioners.
Innovation Solution
The development of supported decision trees that generate medical advice by traversing a decision tree structure based on rhetorical relationships and semantic information from text, allowing for the integration of linguistic cues and explanations, enabling more transparent and personalized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex algorithms are used to provide medical advice and diagnosis, then the accuracy and reliability of medical decisions are improved, but the transparency and interpretability of the system deteriorate
Solution Approach 1:
The patent segments the complex medical decision-making process into a structured decision tree with discrete nodes and edges. Each node represents a specific medical condition or decision point, and each edge represents a transition based on particular criteria. This segmentation transforms an opaque complex algorithm into an interpretable hierarchical structure that maintains diagnostic accuracy while enabling transparency through visualizable decision paths.
Solution Approach 2:
The patent introduces discourse trees as an intermediary layer between the complex medical knowledge base and the final decision output. Discourse trees extract and organize rhetorical relationships from medical texts, creating an intermediate structured representation that bridges the gap between unstructured medical knowledge and interpretable decision rules, thereby maintaining reliability while improving transparency.
2Reliability
If complex algorithms requiring extensive expertise are used, then the reliability of medical decisions is improved, but the ease of operation and accessibility for general practitioners deteriorate
Solution Approach 1:
The patent creates a simplified copy of medical expert decision-making logic in the form of a decision tree structure. Instead of requiring general practitioners to directly engage with complex algorithms, the system copies the essential decision logic into an accessible tree format that can be easily navigated and understood, maintaining the reliability of expert-level decisions while dramatically improving ease of operation.
3Productivity
If automated systems provide medical advice, then the productivity and efficiency are improved, but the transparency and interpretability of the decision-making process deteriorate
Solution Approach 1:
The patent implements a dynamic system that can adaptively traverse the decision tree based on patient-specific inputs while maintaining a complete record of the decision path. The system dynamically selects relevant branches based on patient conditions, providing efficient automated advice generation without losing interpretability, as the traversed path and supporting evidence are preserved and can be presented to users for transparency.
Data Source
AI summary
Systems, devices, and methods discussed herein provide improved decision trees (e.g., supported decision trees). A supported decision tree can be generated by generating discourse trees from various documents associated with a subject. One or more decision chains can be generated from each discourse tree, each decision chain being a sequence of elements comprising a premise and a decision connected by rhetorical relationships. A supported decision tree can be generated from the various decision chains, where the nodes of the decision tree are identified from the elements of the plurality of decision chains and ordered based on a set of predefined priority rules. Subsequent input data can be received and the supported decision tree can be traversed to classify the input data.