Abstract: Systems and methods are described for using inference algorithms and machine learning techniques to generate a clinical knowledge set. The present technology also provides systems and methods for generating feature maps comprised of patient-specific extracted and consolidated clinical features for a patient. The present technology also provides systems and methods for building a patient feature map by applying inference algorithms and a machine-learned clinical knowledge set. Such generated patient feature maps are useful for improving the care of patients.

Abstract: Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

Abstract: Systems and methods are described for implementing an advanced, “research-grade” or “regulatory-grade,” real-world evidence (RWE) approach. The advanced RWE is able to extract a deep phenotype from rich data sources using advanced technologies including artificial intelligence. The rich data sources include both unstructured data and structured data from electric health records and may include additional data sources such as claims or registries. Systems and methods are also described for validating the deep phenotype which can then be used to create a patient cohort that may be linked to exposure or outcome data to make credible clinical assertions.

Abstract: Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

Abstract: Systems and methods are described for implementing an advanced, “research-grade” or “regulatory-grade,” real-world evidence (RWE) approach. The advanced RWE is able to extract a deep phenotype from rich data sources using advanced technologies including artificial intelligence. The rich data sources include both unstructured data and structured data from electric health records and may include additional data sources such as claims or registries. Systems and methods are also described for validating the deep phenotype which can then be used to create a patient cohort that may be linked to exposure or outcome data to make credible clinical assertions.

Abstract: Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

Abstract: Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

Abstract: Systems and methods are described for disambiguating terms, a challenging problem in computational linguistics. An ambiguous term may be regular words or phrases, or preferably abbreviations or acronyms. A potentially ambiguous term may be identified from an information source, and has two or more potential meanings. For each potential meaning, context and frequency of the potential meaning are determined. Context may include section headings, nearby concepts, or all relevant concepts within the information source. Frequency may reflect frequency of the candidate concepts within literature, medical literature, patient records, or another information source. Context, frequency, or both for each potential meaning can support a computer technology algorithm to select one potential meaning over others.

Abstract: Systems and methods are described for implementing an advanced, “research-grade” or “regulatory-grade,” real-world evidence (RWE) approach. The advanced RWE is able to extract a deep phenotype from rich data sources using advanced technologies including artificial intelligence. The rich data sources include both unstructured data and structured data from electric health records and may include additional data sources such as claims or registries. Systems and methods are also described for validating the deep phenotype which can then be used to create a patient cohort that may be linked to exposure or outcome data to make credible clinical assertions.

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: The present invention is directed to tissue closure devices for closing a tissue opening, including one or more closure components, each closure component having a first member and a second member, each of the first and second members having a first surface that adheres to a tissue surface proximate to the tissue opening, each of the first and second members having a second surface substantially orthogonal to the first surface, and each of the first and second members having a transitional region between the first surface and the second surface which is contoured to evert an edge of the tissue opening upon the drawing together of the first and second members. The present invention is also directed to systems and kits, dressing systems, and methods for tissue repair and closure.