Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: The described implementations relate to expressing terminologies in a hierarchical form. One implementation can receive a terminology that can include concept-code pairs. For example, each of the concept-code pairs can include a concept and a code that is assigned to the concept by the terminology. The implementation can map the concepts to levels of a hierarchical ontology, and associate some of the concepts across different levels of the hierarchical ontology. The implementation can also provide programmatic access to the concept-code pairs of the terminology via the hierarchical ontology.

Abstract: A dataset is searched using inclusion set criteria to produce an inclusion set and exclusion set criteria to produce an exclusion set. A set of unique content elements is identified from the inclusion set and the exclusion set. Metrics are derived from the inclusion set, exclusion set, and set of unique content elements, such as a measure, for each unique content element, of the absolute value of the difference between the percentage of records in the inclusion set containing the unique content element and the percentage of records in the exclusion set containing the unique content element. The unique content element set may be sorted and displayed in decreasing order of the above-referenced absolute value. The content element set may be filtered. Individual content elements may be excluded from the content set. A predictive model may be generated based on the resulting version of the content element set.

Abstract: A de-identification system is described herein for converting original messages into de-identified messages. The de-identification system leverages original message-inception-functionality which operates as a gateway for providing original messages for use by a production environment. Namely, the de-identification system includes a transformation module that receives the original messages from the original message-inception functionality. The transformation module then converts instances of sensitive information contained in the original messages into non-sensitive information, to produce the de-identified messages. A de-identified environment can consume the de-identified messages with high confidence that the messages have been properly sanitized. This is because the de-identification work has been performed at a well-contained quarantine level of the message processing functionality.

Abstract: The described implementations relate to patient identification. One implementation can acquire binary biometric data and structured data from a patient. This implementation can compare the acquired patient binary biometric data and structured data to binary biometric data and structured data associated with patient files in an electronic master patient index.

Abstract: The described implementations relate to expressing terminologies in a hierarchical form. One implementation can receive a terminology that can include concept-code pairs. For example, each of the concept-code pairs can include a concept and a code that is assigned to the concept by the terminology. The implementation can map the concepts to levels of a hierarchical ontology, and associate some of the concepts across different levels of the hierarchical ontology. The implementation can also provide programmatic access to the concept-code pairs of the terminology via the hierarchical ontology.

Abstract: The described implementations relate to patient identification. One implementation can acquire binary biometric data and structured data from a patient. This implementation can compare the acquired patient binary biometric data and structured data to binary biometric data and structured data associated with patient files in an electronic master patient index.

Abstract: A de-identification system is described herein for converting original messages into de-identified messages. The de-identification system leverages original message-inception-functionality which operates as a gateway for providing original messages for use by a production environment. Namely, the de-identification system includes a transformation module that receives the original messages from the original message-inception functionality. The transformation module then converts instances of sensitive information contained in the original messages into non-sensitive information, to produce the de-identified messages. A de-identified environment can consume the de-identified messages with high confidence that the messages have been properly sanitized. This is because the de-identification work has been performed at a well-contained quarantine level of the message processing functionality.