Clinical Document Graph Modeling for Semantic Retrieval
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Solution Overview
Problem
Current EHR systems lack the ability to effectively capture and semantically measure clinical concepts in free-text clinical notes, hindering search, comparison, and clustering of large amounts of health data, which affects clinical workflow and cognitive reasoning.
Innovation Solution
A system and method that model free-text clinical documents into a hierarchical graph-like data structure using natural language processing to identify and annotate clinical terms, classify them into clinical event classes, and create links between concepts based on semantic relationships, providing an edge score to indicate similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If free-text clinical documents are stored in unstructured format, then information capacity is maximized, but information retrieval and analysis efficiency deteriorates
Solution Approach 1:
The patent segments unstructured free-text clinical documents into structured components by extracting clinical concepts and organizing them into a graph data structure with concept nodes and edges. This segmentation enables efficient retrieval while preserving the full information content of the original documents.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms unstructured text into a structured graph representation. This intermediary structure serves as a bridge between the original unstructured documents and analysis tools, enabling efficient information retrieval without losing the richness of the source data.
2Productivity
If clinical concepts are extracted and organized into structured data, then information retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal graph data structure that can represent multiple types of clinical information (concepts, relationships, hierarchies) in a unified format. This multi-functional structure handles diverse clinical data types without requiring separate processing systems for each data type, thereby managing complexity.
Solution Approach 2:
The patent transforms one-dimensional linear text into a multi-dimensional graph structure with nodes and edges representing concepts and relationships. This dimensional transformation organizes information hierarchically and relationally, improving retrieval efficiency while providing a systematic framework that manages complexity through structured organization.
3Measurement precision
If semantic relationships between clinical concepts are modeled, then analytical capability is enhanced, but processing time increases
Solution Approach 1:
The patent performs preliminary extraction and organization of clinical concepts and their semantic relationships during the initial processing stage, creating a pre-structured graph representation. This preliminary action enables faster subsequent analysis by having the semantic structure already in place, rather than computing it repeatedly during each analysis operation.
Data Source
AI summary
The present disclosure pertains to modeling free-text clinical documents into a hierarchical graph-like data structure based on semantic relationships among clinical concepts present in the documents. A method comprises parsing, identifying, and annotating clinical terms within free-text clinical documents. This is accomplished by storing identified clinical terms in a concept node. The concept node is a data structure that has a set of properties to categorize stored concepts. Clinical concepts of free-text clinical documents are classified into clinical event classes. The free-text clinical documents include clinical terms that were associated with clinical concept categories. Classifying clinical concepts includes organizing clinical text-free documents into sections that describe a specific aspect of the clinical text-free documents that include one or more of clinical, technical, or administrative aspects of the documents. Links are provided between clinical concepts such that individual clinical concepts correspond to individual concept nodes.