Semantic Search Engine for Clinical Documents
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Solution Overview
Problem
Current clinical search engines rely on simple keyword matching, leading to information overload and cognitive burden for clinicians, resulting in suboptimal information synthesis and increased risks of errors due to unretrieved patient data in electronic health records.
Innovation Solution
A semantic search system that uses a client web interface, a semantic analysis engine, and a NoSQL database to extract and present clinically relevant information based on the semantic meaning of user-entered keywords, leveraging natural language processing to identify and map keywords to UMLS concepts and retrieve related concepts from clinical notes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple keyword matching is used for clinical search, then the search system is simple and fast, but the search results are not contextually relevant and cause information overload
Solution Approach 1:
The patent introduces an intermediary layer between keyword input and search results: a thesaurus and semantic network that expands and refines search terms. This intermediary processes keywords through synonym expansion, hypernym retrieval, and semantic relationship mapping to generate more relevant results without requiring complete system redesign
Solution Approach 2:
The search system is segmented into distinct functional modules: keyword processing module, thesaurus lookup module, semantic network traversal module, and result ranking module. This segmentation allows each component to specialize in specific tasks while maintaining overall system manageability and scalability
2Reliability
If manual retrieval of active diagnoses is performed by clinicians, then the workflow is simple, but the cognitive burden increases and errors occur due to information overload
Solution Approach 1:
The system enables self-service by automatically performing diagnosis retrieval and presentation based on clinician input. The automated system handles thesis expansion, semantic search, and result filtering without requiring manual cognitive processing, thereby reducing errors while maintaining workflow simplicity
Solution Approach 2:
The patent replaces the mechanical manual search process with an automated computational system that uses semantic networks and thesauri to perform diagnosis retrieval. This substitution eliminates cognitive burden while preserving ease of operation through natural language interface
3Loss of information
If extensive clinical documents are reviewed to find active diagnoses, then comprehensive information is available, but the time required increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-building and maintaining semantic networks and thesauri that encode relationships between medical concepts. This pre-computed knowledge structure enables rapid retrieval without requiring real-time analysis of extensive documents, thus maintaining information completeness while improving speed
Solution Approach 2:
The semantic network acts as an intermediary that pre-organizes clinical knowledge relationships, allowing the system to quickly navigate to relevant diagnoses without scanning extensive raw documents. This intermediary structure enables efficient information retrieval while preserving comprehensive coverage
Data Source
AI summary
When performing semantic search for related clinical concepts based on their semantic meaning, a semantic search engine framework employs a client web interface (12) (e.g., a browser) that receives keyword search information (14), and a web server (16) that communicates with a semantic analysis engine (18). A natural language processing (NLP) engine (server) (22) receives and stores clinical notes and information (24), extracts clinical concepts from the clinical notes and stores them in a NoSQL database (26). The NLP engine converts unstructured free text notes into structured actionable data. The semantic analysis engine analyses user-entered keywords, maps them to a UMLS concept, and identifies related concepts based on one or more relational knowledge sources. The web server searches for the related concept IDs in the NoSQL database and returns a list (28) of relevant notes and concepts for display to, and selection by, the user.