Abstract: A system includes a processor and memory. The memory stores a model database including models and a classification database including classification scores corresponding to an input. The memory stores instructions for execution by the processor. The instructions include, in response to receiving a first input from a user device of a user, determining, for the first input, classification scores for classifications by applying the models to the first input. Each model determines one of the classification scores. The instructions include storing the classification scores as associated with the first input in the classification database and identifying the first input as within a first classification in response to a first classification score corresponding to the first classification exceeding a first threshold. The instructions include transmitting, for display on an analyst device, the first input based on the first classification to a first analyst queue associated with the first classification.

Abstract: A computer system for transforming a user interface according to data store mining includes a data store configured to store a parameter related to a user and index event data of a set of events. A data processing circuit is configured to identify a first set of identifiers and train a machine learning model based on event data by the data store. An interface circuit is configured to receive an indication of a selected identifier of the plurality of identifiers, determine a first intake metric of the selected identifier using the machine learning model, and a second intake metric of the selected identifier and the parameter using the machine learning model. The interface circuit is configured to transform the user interface according to the first intake metric and the second intake metric.

Abstract: A method includes obtaining source data containing feedback information, identifying different phrases of interest in the feedback information, generating count data that indicates how often the different phrases of interest appear in the feedback information, determining counts of how often the phrases of interest appear in the feedback information, and modifying a count that is representative of how often at least one phrase of interest appears in the feedback data. The count is modified by reducing the count by a count of how often another, shorter phrase of interest also appears in the feedback data. The method also includes generating at least one interface respectively reflecting the count data for the different phrases of interest and the count that has been modified.

Abstract: A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

Abstract: A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

Abstract: A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

Abstract: A computer system includes an input configured to receive a first image of medication located in a receptacle, memory, and a processor configured to execute instructions including creating a second image based on the first image, dividing pixels of the second image into first and second subsets, and scanning the second image along a first axis to count, for each point along the first axis, a number of pixels in the first subset along a line perpendicular to the first axis that intersects the first axis at the point. The instructions also include estimating positions of first and second edges of the receptacle along the first axis based on the counts of the pixels, defining an opening of the receptacle based on the estimated positions of the first and second edges, and outputting a processed image that indicates areas of the image that are outside of the defined opening.

Abstract: Data threat evaluation systems and methods are described. A data model structure includes a root object query that, when executed, returns a third data subset from the plurality of data types that predate a known threat, the third data subset including data types in both the first data subset and the second data subset; and a model schema to extract, from the third data subset, data types of the first subset that predicate and indicate the threat, the model schema to produce at least an individualized data threat regression model, a script originator regression model, and a script filler data threat regression model using the extracted data types. The system may use the individualized data threat regression model, the script originator regression model, and the script filler data threat regression model back on the data set to identify potential threats. The system can be applied as a fraud, waste or abuse detector.

Abstract: A method includes capturing a first image of medication held by a receptacle. The method includes creating a second image based on the first image. The method includes determining a first subset of pixels of the second image that are more likely to correspond to the receptacle. The method includes processing the second image along a first axis by, for each point: defining a line perpendicularly intersecting the first axis at the point and counting how many of the pixels located along the line are in the first subset. The method includes determining first and second local maxima of the counts. The method includes estimating positions of first and second edges of the receptacle based on positions of the local maxima. The method includes defining an ellipse based on the first and second edges and excluding areas of the first image outside the defined ellipse from further processing.

Abstract: A method includes capturing a first image of medication held by a receptacle. The method includes creating a second image based on the first image. The method includes determining a first subset of pixels of the second image that are more likely to correspond to the receptacle. The method includes processing the second image along a first axis by, for each point: defining a line perpendicularly intersecting the first axis at the point and counting how many of the pixels located along the line are in the first subset. The method includes determining first and second local maxima of the counts. The method includes estimating positions of first and second edges of the receptacle based on positions of the local maxima. The method includes defining an ellipse based on the first and second edges and excluding areas of the first image outside the defined ellipse from further processing.