Ontology-Based Record Classification for Complex Query Reasoning
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Solution Overview
Problem
Traditional computerized systems require vast numbers of procedural rules to evaluate complex queries in electronic health records, leading to memory consumption, processing resource utilization, and structural incompatibility across databases, necessitating frequent code updates for condition and state changes.
Innovation Solution
A hybrid procedure-based and ontology-based data evaluation system that uses a knowledge-based library and reasoner to classify database records, reducing the need for customized procedural code by mapping database fields to entities and inferring classes based on asserted relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional procedural code is used for complex querying in electronic health record systems, then data evaluation can be performed, but the system requires a vast number of rules and significant processing resources
Solution Approach 1:
The patent introduces an intermediary layer (ontology model and reasoner) between the procedural code and the data evaluation process. This intermediary translates complex procedural queries into ontological reasoning operations, reducing the number of explicit rules needed while maintaining evaluation accuracy. The ontology serves as a mediator that encapsulates domain knowledge and relationships, allowing the system to infer results without requiring exhaustive procedural rule sets.
2Reliability
If traditional procedural code is used for complex querying, then data evaluation can be performed, but memory consumption is high
Solution Approach 1:
The patent creates a simplified ontological copy or representation of the complex procedural logic. Instead of storing and processing the full procedural rule sets in memory, the system uses a compressed ontological model that captures the essential relationships and constraints. This ontological representation requires significantly less memory while enabling the same data evaluation capabilities through logical inference.
3Adaptability or versatility
If traditional procedural code is used, then complex querying can be performed, but the system requires extensive updates with changes in conditions or states
Solution Approach 1:
The patent implements a dynamic ontology model that can adapt to changing conditions and states without requiring extensive reprogramming. The ontological framework allows for flexible addition, modification, and removal of concepts and relationships through structured updates rather than rewriting procedural code. This dynamic structure enables the system to accommodate changing medical conditions, data formats, and querying requirements while maintaining stability in the core evaluation engine.
4Reliability
If traditional procedural code is used for data evaluation, then querying can be performed, but processing efficiency is low
Solution Approach 1:
The patent replaces the mechanical procedural execution system with an ontological reasoning system. Instead of mechanically stepping through complex procedural code and rule evaluations, the system uses logical inference mechanisms based on ontological relationships. This substitution transforms the processing paradigm from sequential procedural execution to parallel logical deduction, significantly improving processing efficiency while maintaining result accuracy.
Data Source
AI summary
Systems and methods are described for procedurally-based decision support using ontology-based classification of database records. The procedural decision support can facilitate extraction of contextually relevant data from a database. The data may be formatted for compatibility with a knowledge-based data library using one or more scripts and populated in the library as an entity. Classification of the entity can be reasoned using the available data. One or more classifications of the entity may be returned to the procedural decision support to facilitate computation of a recommendation.