Abstract: Improved systems and methods for extracting information from medical and natural-language text data.

Abstract: Systems and methods described herein are for transforming narrative content into structured output. In some embodiments the narrative content is processed using a natural language processing (NLP) engine and a clinical model. The structured output can include a section, a clinical assertion, and a plurality of elements, wherein the elements may include section elements and clinical assertion elements that annotate the section and clinical assertions respectively. The elements can be labeled based on the clinical model.

Abstract: Systems and methods described herein are for transforming narrative content into structured output. In some embodiments the narrative content is processed using a natural language processing (NLP) engine and a clinical model. The structured output can include a section, a clinical assertion, and a plurality of elements, wherein the elements may include section elements and clinical assertion elements that annotate the section and clinical assertions respectively. The elements can be labeled based on the clinical model.

Abstract: Systems and methods described herein are for transforming narrative content into structured output. In some embodiments the narrative content is processed using a natural language processing (NLP) engine and a clinical model. The structured output can include a section, a clinical assertion, and a plurality of elements, wherein the elements may include section elements and clinical assertion elements that annotate the section and clinical assertions respectively. The elements can be labeled based on the clinical model.

Abstract: Systems and methods described herein are for transforming narrative content into structured output. In some embodiments the narrative content is processed using a natural language processing (NLP) engine and a clinical model. The structured output can include a section, a clinical assertion, and a plurality of elements, wherein the elements may include section elements and clinical assertion elements that annotate the section and clinical assertions respectively. The elements can be labeled based on the clinical model.

Abstract: Improved systems and methods for extracting information from medical and natural-language text data.

Abstract: The present invention also relates to natural language processing and extraction of relational information associated with genes and proteins that are found in genomics journal articles. To enable access to information in textual form, the natural language processing system of the present invention provides systems and methods for extracting and structuring information found in the literature in a form appropriate for subsequent applications.

Abstract: Systems and methods for extracting and encoding genotype-phenotype information from journal articles and other publications are provided. In some embodiments, the disclosed subject matter includes a preprocessor, boundary identifier, parser, phrase recognizer and an encoder to convert natural-language input text and parameters into structured text. The structured text can take the form of codes which account for genotype-phenotype information and are compatible with a controlled vocabulary.

Abstract: The present invention also relates to natural language processing and extraction of relational information associated with genes and proteins that are found in genomics journal articles. To enable access to information in textual form, the natural language processing system of the present invention provides systems and methods for extracting and structuring information found in the literature in a form appropriate for subsequent applications.

Abstract: The present invention relates to methods for identifying novel genes comprising: (i) generating one or more specialized databases containing information on gene/protein structure, function and/or regulatory interactions; and (ii) searching the specialized databases for homology or for a particular motif and thereby identifying a putative novel gene of interest. The invention may further comprise performing simulation and hypothesis testing to identify or confirm that the putative gene is a novel gene of interest. The present invention also relates to natural language processing and extraction of relational information associated with genes and proteins that are found in genomics journal articles. To enable access to information in textual form, the natural language processing system of the present invention provides a method for extracting and structuring information found in the literature in a form appropriate for subsequent applications.

Abstract: The present invention relates to methods for identifying novel genes comprising: (i) generating one and/or more specialized databases containing information on gene/protein structure, function and/or regulatory interactions; and (ii) searching the specialized databases for homology or for a particular motif and thereby identifying a putative novel gene of interest. The invention may further comprise performing simulation and hypothesis testing to identify or confirm that the putative gene is a novel gene of interest. The present invention also relates to natural language processing and extraction of relational information associated with genes and proteins that are found in genomics journal articles. To enable access to information in textual form, the natural language processing system of the present invention provides a method for extracting and structuring information found in the literature in a form appropriate for subsequent applications.

Abstract: A computerized method for extracting information from natural-language text data includes parsing the text data to determine the grammatical structure of the text data and regularizing the parsed text data to form structured word terms. The parsing step, which can be performed in one or more parsing modes, includes the step of referring to a domain parameter having a value indicative of a domain from which the text data originated, wherein the domain parameter corresponds to one or more rules of grammar within a knowledge base related to the domain to be applied for parsing the text data. Preferably, the structured output is mapped back to the words in the original sentences of the text data input using XML tags.

Abstract: In computerized processing of natural-language medical/clinical data including phrase parsing and regularizing, parameters are referred to whose value can be specified by the user. Thus, a computerized system can be provided with versatility, for the processing of data originating in diverse domains, for example. Further to a parser and a regularizer, the system includes a preprocessor, output filters, and an encoding mechanism.