Systems and methods for semantic search and extraction of related concepts from clinical documents

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.

Description

technical field [0001] The present invention finds particular application in patient healthcare data systems and methods. However, it should be understood that the described techniques may also be applied to other document management systems, other data management techniques, and the like. Background technique [0002] As more and more patient health records become digital, much work has been done on clinical concept extraction, which maps clinical records to unified medical terminology, such as UMLS, SNOMED-CT. Such work converts unstructured free-text clinical records into a structured coded format that is more suitable for further information retrieval, including search functions. Current clinical search engines are primarily based on simple keyword matching. [0003] Over the past few decades, clinical research interest within academia and industry has increasingly focused on extracting important patient details, such as findings and issues from clinical documentation,...

AI Technical Summary

Problems solved by technology

However, the plugin does not provide many of the needed context-aware, semantically relevant search results that are useful for navigation and research-oriented viewing of large volumes of free-text patient reports both within and outside the radiology domain

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