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: Embodiments of the present invention enable processing of concept-based query requests submitted to a query service within a healthcare transaction framework. In embodiments, a terminology service within the healthcare transaction framework manages terminology content (concepts represented by a set of terminologies). In embodiments, search criteria from a data access query request are submitted to the terminology service to extract a set of concepts from the terminology content that are associated with the search criteria. In embodiments, equivalence search criteria include a seed concept, and the terminology service returns a set of equivalent concepts comprising the seed concept. In embodiments, classification search criteria include a classification identifier, and the terminology service returns a set of concepts associated with the classification identifier. In embodiments, the query service generates and submits a data access query comprising predicates associated with the returned set of concepts.

Abstract: Embodiments of the present invention enable processing of concept-based query requests submitted to a query service within a healthcare transaction framework. In embodiments, a terminology service within the healthcare transaction framework manages terminology content (concepts represented by a set of terminologies). In embodiments, search criteria from a data access query request are submitted to the terminology service to extract a set of concepts from the terminology content that are associated with the search criteria. In embodiments, equivalence search criteria include a seed concept, and the terminology service returns a set of equivalent concepts comprising the seed concept. In embodiments, classification search criteria include a classification identifier, and the terminology service returns a set of concepts associated with the classification identifier. In embodiments, the query service generates and submits a data access query comprising predicates associated with the returned set of concepts.

Abstract: Described herein are systems and methods for processing data. In some embodiments, a system may include a natural language processing (NLP) engine configured to transform a data set into a plurality of concepts within a plurality of distinct contexts, an ontology configured to structure the plurality of concepts by annotating relationships between and creating aggregations of the concepts, and a data mining engine configured to process the relationships of the concepts and to identify associations and correlations in the data set. In some embodiments, the method may include the steps of receiving a data set, scanning the data set with a natural language processing (NLP) engine to identify a plurality of concepts within a plurality of distinct contexts, structuring the data set with an ontology by creating aggregations of the concepts and annotating relationships between the concepts, and identifying patterns in the relationships between the plurality of concepts.

Abstract: Described herein are systems and methods for transforming a speech input into machine-interpretable structured data. In some embodiments, a system may include an automated speech recognition (ASR) engine configured to receive a live speech input and to continuously generate a text of the live speech input, a natural language processing (NLP) engine configured to transform the text into machine-interpretable structured data, and a user interface device configured to display the live speech input and a corresponding portion of the structured data in a predetermined order with respect to the structured data. In some embodiments, the method may include the steps of receiving a speech input with a speech capture component of a user interface device, generating a text from the speech input, identifying textual cues in the text, modifying the text based on the textual cues, and transforming the modified text into machine-interpretable structured data.

Abstract: Embodiments of the present invention enable processing of concept-based query requests submitted to a query service within a healthcare transaction framework. In embodiments, a terminology service within the healthcare transaction framework manages terminology content (concepts represented by a set of terminologies). In embodiments, search criteria from a data access query request are submitted to the terminology service to extract a set of concepts from the terminology content that are associated with the search criteria. In embodiments, equivalence search criteria include a seed concept, and the terminology service returns a set of equivalent concepts comprising the seed concept. In embodiments, classification search criteria include a classification identifier, and the terminology service returns a set of concepts associated with the classification identifier. In embodiments, the query service generates and submits a data access query comprising predicates associated with the returned set of concepts.

Abstract: Embodiments of the present invention enable processing of concept-based query requests submitted to a query service within a healthcare transaction framework. In embodiments, a terminology service within the healthcare transaction framework manages terminology content (concepts represented by a set of terminologies). In embodiments, search criteria from a data access query request are submitted to the terminology service to extract a set of concepts from the terminology content that are associated with the search criteria. In embodiments, equivalence search criteria include a seed concept, and the terminology service returns a set of equivalent concepts comprising the seed concept. In embodiments, classification search criteria include a classification identifier, and the terminology service returns a set of concepts associated with the classification identifier. In embodiments, the query service generates and submits a data access query comprising predicates associated with the returned set of concepts.