Abstract: A system and method for facilitating the processing and the use of documents by providing a system for categorizing document section headings under a set of canonical section headings. In the method for categorizing section headings, there may be a process of training a database and matching methods to categorize different but equivalent document section headings under canonical headings and categories. Once trained, the system may match and categorize the document sections with little to no supervision of the categorization for large sets of documents.

Abstract: A system and method for facilitating the processing and the use of documents by providing a system for categorizing document section headings under a set of canonical section headings. In the method for categorizing section headings, there may be a process of training a database and matching methods to categorize different but equivalent document section headings under canonical headings and categories. Once trained, the system may match and categorize the document sections with little to no supervision of the categorization for large sets of documents.

Abstract: The invention includes a medical document handling system and method and automated coding systems and methods for assigning predetermined medical codes to medical documents based on the documents' contents. The invention functions by analyzing electronic medical records and extracting medical information using natural language processing and machine learning. The system collects and amalgamates medical documentation in various formats from multiple sources and locations, normalizes the information, analyzes the information, recognizes information indicating contents corresponding to classification codes, assigns classification codes, and presents information in context correlated to medical records for billing and other purposes.

Abstract: A system and method for facilitating the processing and the use of documents by providing a system for categorizing document section headings under a set of canonical section headings. In the method for categorizing section headings, there may be a process of training a database and matching methods to categorize different but equivalent document section headings under canonical headings and categories. Once trained the system may match and categorize the document sections with little to no supervision of the categorization for large sets of documents.

Abstract: The invention includes a medical document handling system and method and automated coding systems and methods for assigning predetermined medical codes to medical documents based on the documents' contents. The invention functions by analyzing electronic medical records and extracting medical information using natural language processing and machine learning. The system collects and amalgamates medical documentation in various formats from multiple sources and locations, normalizes the information, analyzes the information, recognizes information indicating contents corresponding to classification codes, assigns classification codes, and presents information in context correlated to medical records for billing and other purposes.

Abstract: An information retrieval system that represents the content of a language-based database being searched as well as the user's natural language query. In accordance with one aspect of the invention, the information retrieval system includes a non-real-time development system for automatically creating a database index having one or more content-based database keywords of the data base; and a real-time retrieval system that, in response to a user's natural language query, searches the keyword index for one or more content-based query keywords derived from the natural language query. The development system and the retrieval system morphologically, syntactically and linguistically analyze the data base and the natural language query, respectively, to generate the one or more database keywords and query keywords representing the content of the database and the natural language query, respectively.