Abstract: Various embodiments of the present disclosure provide automated data compliance techniques for complex access controlled datasets subject to a plurality of data access constraints. Some of the techniques may include generating, using one or more natural language models, entity-relationship data for an access controlled dataset and generating a knowledge graph based on the entity-relationship data. The knowledge graph includes a plurality of vertices connected by a plurality of edges that may be traversed to identify a data access condition indicative of a data access violation or a data coverage violation. Some of the techniques may include generating, using the knowledge graph, a natural language condition description based on the data access condition and providing a condition alert indicative of the natural language condition description.

Abstract: Various embodiments of the present disclosure provide automated data compliance techniques for complex access controlled datasets subject to a plurality of data access constraints. Some of the techniques may include generating, using one or more natural language models, entity-relationship data for an access controlled dataset and generating a knowledge graph based on the entity-relationship data. The knowledge graph includes a plurality of vertices connected by a plurality of edges that may be traversed to identify a data access condition indicative of a data access violation or a data coverage violation. Some of the techniques may include generating, using the knowledge graph, a natural language condition description based on the data access condition and providing a condition alert indicative of the natural language condition description.

Abstract: An example path discovery system includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to obtain routing script metadata associated with a telephony network, to parse routing logic implemented by one or more nodes of the telephony network, to emulate, using the routing script metadata and the parsed routing logic, a traversal path for a call through the telephony network, the emulated traversal path including the one more nodes for which the routing logic is parsed, to store, to the memory, the emulated traversal path and respective final states of runtime variables associated with the one or more nodes included in the emulated traversal path, and to generate, in a machine-readable format, one or more reports indicating a destination of the emulated traversal path in the telephony network and an ingress of the emulated traversal path with respect to the telephony network.

Abstract: A method of improving the performance of an information computer system for an enterprise includes cataloging information artifacts from the information computer system. The information artifacts represent data about intellectual assets and interrelationships between intellectual assets within the enterprise. Knowledge maps are created representing the cataloged information artifacts within a knowledgebase. A knowledge map relevant to a user is identified. The identified knowledge map is displayed to the user via an output.

Abstract: An example path discovery system includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to obtain routing script metadata associated with a telephony network, to parse routing logic implemented by one or more nodes of the telephony network, to emulate, using the routing script metadata and the parsed routing logic, a traversal path for a call through the telephony network, the emulated traversal path including the one more nodes for which the routing logic is parsed, to store, to the memory, the emulated traversal path and respective final states of runtime variables associated with the one or more nodes included in the emulated traversal path, and to generate, in a machine-readable format, one or more reports indicating a destination of the emulated traversal path in the telephony network and an ingress of the emulated traversal path with respect to the telephony network.

Abstract: A computing entity accesses instances of medical information corresponding to a population of patients and generates a plurality of medical sentences corresponding to the population by, for each patient of the population, generating one medical sentence corresponding to the patient and a timestamp associated with each instance such that the one medical sentence comprises one or more medical codes in a chronological order. The computing entity generates a vector dictionary comprising a plurality of multi-dimensional vectors based on a vector generation model trained using machine learning and the plurality of medical sentences with each multi-dimensional vector corresponding to a medical code. The computing entity generates a digital representation of a first patient of the population using an anagram model and the vector dictionary, determines a health state of the first patient based on the digital representation, and provides an output indicating the health state of the first patient.

Abstract: A three-dimensional visualization of patient medical information is provided via a user interface of a user computing entity. Responsive to receiving user input, the user computing entity transmits a visualization request that comprises a patient indication and a desired visualization type indication. The user computing entity receives a three-dimensional visualization corresponding to the patient indication and the desired visualization type indication. The three-dimensional visualization is generated by mapping one or more medical codes each to a corresponding set of x, y, z coordinates. The three-dimensional visualization is displayed via the interactive user interface and comprises (a) a graphical representation of a human body corresponding to the x, y, z coordinates and (b) one or more points and/or zones. Each point and/or zone (i) is mapped to a location on the graphical representation of the human body via a body coordinate and (ii) corresponds to a medical code.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts (e.g., neural networks) to determine predicted relevancy scores for transactions, sort the transactions based on the same, and generate and provide a user interface based at least in part on the scores for presentation to an end user.

Abstract: A computing entity accesses instances of medical information corresponding to a population of patients and generates a plurality of medical sentences corresponding to the population by, for each patient of the population, generating one medical sentence corresponding to the patient and a timestamp associated with each instance such that the one medical sentence comprises one or more medical codes in a chronological order. The computing entity generates a vector dictionary comprising a plurality of multi-dimensional vectors based on a vector generation model trained using machine learning and the plurality of medical sentences with each multi-dimensional vector corresponding to a medical code. The computing entity generates a digital representation of a first patient of the population using an anagram model and the vector dictionary, determines a health state of the first patient based on the digital representation, and provides an output indicating the health state of the first patient.

Abstract: A three-dimensional visualization of patient medical information is provided via a user interface of a user computing entity. Responsive to receiving user input, the user computing entity transmits a visualization request that comprises a patient indication and a desired visualization type indication. The user computing entity receives a three-dimensional visualization corresponding to the patient indication and the desired visualization type indication. The three-dimensional visualization is generated by mapping one or more medical codes each to a corresponding set of x, y, z coordinates. The three-dimensional visualization is displayed via the interactive user interface and comprises (a) a graphical representation of a human body corresponding to the x, y, z coordinates and (b) one or more points and/or zones. Each point and/or zone (i) is mapped to a location on the graphical representation of the human body via a body coordinate and (ii) corresponds to a medical code.