Concept Vector Annotation for Electronic Data Record Similarity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Healthcare data entry and access are hindered by the complexity of terminology standards like SNOMED CT, which require medical staff to manage over 300,000 terms, making it impractical for nurses and physicians to efficiently enter and access data while attending to patients.
Innovation Solution
A system that computes concept vectors for electronic data records using an ontology, creating a similarity network to represent similarities between records, allowing for automated annotation and enhanced search capabilities through kernel-based algorithms and similarity-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If medical staff manually enter and manage data according to terminology standards like SNOMED CT, then data accuracy and standardization are improved, but the time required and operational complexity increase significantly
Solution Approach 1:
The system performs automated annotation of electronic data records using concept vectors and similarity networks, eliminating the need for manual data entry and management by medical staff. The annotation unit automatically processes unstructured data and assigns standardized terminology, allowing the system to serve itself rather than requiring human intervention for data standardization.
Solution Approach 2:
The patent replaces the manual mechanical process of data entry with an automated computational system. The annotation unit uses concept vectors, ontology relationships, and similarity networks to automatically annotate electronic data records, substituting human cognitive and manual operations with algorithmic processing that maintains standardization without time loss.
2Measurement precision
If medical staff learn and follow complex terminology standards with 300,000+ terms, then data access accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system introduces an intermediary annotation unit that acts as a bridge between unstructured electronic data and standardized terminology. Instead of requiring medical staff to directly interact with complex terminology standards, the annotation unit automatically processes data and applies appropriate terminology, maintaining accuracy while preserving ease of operation.
Solution Approach 2:
The patent extracts the complex terminology management task from the user's responsibility and transfers it to the automated annotation system. The annotation unit handles the complexity of ontology relationships and concept matching internally, allowing users to access data accurately without needing to learn or manage the underlying 300,000+ term vocabulary.
3Productivity
If automated annotation using concept vectors and ontology is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional units: an annotation unit for computing concept vectors and applying ontology, and a similarity network unit for computing relationships between records. This segmentation allows each unit to specialize in specific tasks, improving overall productivity while managing complexity through modular architecture where each component has a defined responsibility.
Data Source
AI summary
A system and method for analyzing electronic data records including an annotation unit being operable to receive a set of electronic data records and to compute concept vectors for the set of electronic data records, wherein the coordinates of the concept vectors represent scores of the concepts in the respective electronic data record and wherein the concepts are part of an ontology, a similarity network unit being operable to compute a similarity network by means of the concept vectors and by at least one relationship between the concepts of the ontology, the similarity network representing similarities between the electronic data records, wherein the vertices of the similarity network represent the electronic data records and the edges of the similarity network represent similarity values indicating a degree of similarity between the vertices and steps for executing the system.