Diagnosis Engine Syntactic-Semantic Alignment for Medical Misdiagnosis
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Solution Overview
Problem
Current medical diagnosis methods are prone to errors due to their complexity, ambiguity, and reliance on manual efforts, leading to misdiagnosis, especially in cases involving multiple symptoms and nonspecific conditions, where physicians may apply biased heuristics and rely on past experiences rather than comprehensive analysis.
Innovation Solution
A Diagnosis Engine utilizing multi-case-based reasoning through syntactic-semantic alignment and discourse analysis, which splits patient complaints into fragments, matches them with previously classified medical cases, and verifies relationships using extended discourse trees to provide accurate diagnoses by enforcing consensus across multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual diagnosis methods are used by physicians, then diagnostic experience and intuition can be applied, but errors and misdiagnosis increase due to complexity, ambiguity, and biased heuristics
Solution Approach 1:
The patent segments the diagnosis process into multiple independent components: discourse analysis module that parses patient complaints into structured elements, symptom extraction module that identifies individual symptoms, case-based reasoning module that compares against multiple stored cases, and consensus module that integrates results. This segmentation reduces errors by ensuring each component performs a specific function reliably, while the overall system remains manageable through modular architecture.
2Measurement precision
If comprehensive analysis of multiple symptoms is performed, then diagnostic precision improves, but complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring medical case data before actual diagnosis occurs. Discourse trees are built in advance from patient complaints, symptoms are pre-extracted and categorized, and multiple cases are pre-loaded into the reasoning system. When a diagnosis is needed, the system quickly queries against this pre-organized data structure rather than analyzing raw data from scratch, significantly reducing diagnosis time while maintaining comprehensive analysis of all symptoms.
3Reliability
If multiple cases are analyzed through syntactic-semantic alignment and discourse analysis, then misdiagnosis is reduced and classification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent introduces discourse trees as an intermediary data structure that mediates between raw patient complaints and the case-based reasoning system. The discourse analysis module transforms unstructured complaint text into structured discourse trees representing rhetorical relationships between statements. This intermediary structure enables systematic syntactic-semantic alignment with stored cases through standardized comparison protocols, improving classification accuracy while making the computational process more efficient by working with structured rather than raw text.
Data Source
AI summary
Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to generate a diagnosis for input symptoms from labels (e.g., diseases, illnesses, and/or conditions) associated with previously-known cases. Extended discourse trees may be generated that identify multiple discourse trees corresponding to various fragment granularities (e.g., document, paragraph, sentence, phrase, word, etc.) of previously-known cases as well as rhetorical relations between those discourse trees. New symptoms can be provided (e.g., via the autonomous agent) as input. The input can be parsed to identify various fragments of the input and rhetorical relations between the fragments. These fragments can be matched to fragments of previously-known cases by matching nodes of the extended discourse tree. If the rhetorical relations between the input fragments match the rhetorical relations indicated in the extended discourse tree of a previously-known cases, the input may be classified with the same label as those previously-known cases.