AI Semantic Analysis for Precision Medicine Data Matching
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Solution Overview
Problem
Medical practitioners face challenges in making timely and informed treatment decisions due to the limitations of keyword searches in vast medical databases, often missing relevant information that could impact patient outcomes.
Innovation Solution
An AI platform generates numerical representations of patient data, including multiomic and text-based parameters, to identify similar cases and provide personalized treatment recommendations by aggregating diverse data sources, reducing human error and improving data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If keyword search is used to find medical information in databases, then the search process is simple and quick, but the information found may be incomplete or suboptimal, leading to missed relevant treatment information
Solution Approach 1:
The patent replaces the mechanical keyword search system with an AI-based semantic analysis system. The AI model understands the context and meaning of medical queries, transforming unstructured medical data into structured insights that capture relevant treatment information more comprehensively than traditional keyword matching.
Solution Approach 2:
The patent changes the search parameters from simple keyword matching to multi-dimensional semantic analysis. By analyzing the contextual meaning, relationships, and semantics of medical terms rather than just exact keyword matches, the system retrieves more relevant and comprehensive treatment information.
2Reliability
If physicians manually review medical databases to find relevant treatment information, then thorough analysis is possible, but the time required exceeds available clinical decision-making time
Solution Approach 1:
The patent performs preliminary analysis of medical data by pre-processing and structuring medical records, research articles, and treatment data before clinical needs arise. The AI model continuously learns from and analyzes medical literature and patient data, so when a physician needs treatment information, the analysis is already complete or near-complete.
Solution Approach 2:
The patent introduces an AI-based information retrieval system as an intermediary between the medical database and the physician. This intermediary automatically processes, structures, and presents relevant treatment information, eliminating the need for physicians to manually search and review databases while maintaining high accuracy.
3Adaptability or versatility
If broad disease population guidelines are used for treatment decisions, then general applicability is achieved, but individual patient specificity is lost
Solution Approach 1:
The patent applies local quality by tailoring treatment recommendations to individual patient characteristics. While maintaining the framework of general disease guidelines, the AI system analyzes specific patient data (medical history, genetic information, current condition) to customize treatment plans, ensuring both general applicability and individual precision.
Solution Approach 2:
The patent segments treatment recommendations into general guideline components and patient-specific customized components. The AI system separates broad disease population guidelines from individual patient factors, allowing physicians to apply general principles while adjusting for specific patient needs based on analyzed medical data.
Data Source
AI summary
Embodiments provide systems and methods for supporting a medical assessment of a target digital entity. To facilitate the medical assessment, a numerical representation of a target digital entity is generated based on at least a portion of source data associated with the target digital entity, and the numerical representation of the target digital entity is compared to numerical representations of a plurality of digital entities to generate similarity values. Each of the similarity values representing a correspondence between the numerical representations of the target digital entity and the plurality of digital entities. Based on the similarity values, one or more candidate digital entities that are similar to the target digital entity are identified. In some aspects, keywords associated with the target digital entity are used to identify an article associated with a diagnosis or treatment of the target digital entity.