Abstract: 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.

Abstract: Systems and methods are disclosed for extracting structured relation information from Chinese clinical notes. Structured relation information in the form of entity-feature-value (EFV) triples is disclosed. The EFV triples may be used for advanced data abstraction. An ontology-driven method for data extraction is disclosed. The ontology-driven method may include lexical and syntactic analysis followed by semantic analysis to generate the EFV triples.

Abstract: A system for uniformly correlating unstructured entry features included in unstructured therapy entries to associated therapy features in structured therapy information is disclosed. The system obtains unstructured therapy entries and identifies unstructured entry features within the individual unstructured therapy entries. The unstructured therapy entry features are correlated to corresponding associated therapy features. The correlation of unstructured entry features to associated therapy features is based on contextual information associated with the individual unstructured therapy entries. Contextual information associated with the unstructured therapy entry includes the syntax of the unstructured therapy entry, a creator of the unstructured therapy entry, and/or the format of the unstructured therapy entry.

Abstract: 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.

Abstract: Systems and methods are disclosed for extracting structured relation information from Chinese clinical notes. Structured relation information in the form of entity-feature-value (EFV) triples is disclosed. The EFV triples may be used for advanced data abstraction. An ontology-driven method for data extraction is disclosed. The ontology-driven method may include lexical and syntactic analysis followed by semantic analysis to generate the EFV triples.