Abstract: A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

Abstract: A method includes receiving historical data collected from a client associated with members. The historical data includes per-member metrics for the client and demographic information for the members. The method includes identifying therapeutic classes for the client based on the per-member metrics and the demographic information. The method includes segmenting the historical data into a data set for each therapeutic class. The method includes, for each therapeutic class of the set of therapeutic classes, determining a pattern for the per-member metrics corresponding to the respective therapeutic class, generating a respective predictive model based on the pattern, and training a neural network of the respective predictive model using a two-stage training process. The predictive model is configured to generate, as output for the therapeutic class, a per-member metric prediction for an input period of future time.

Abstract: A method includes storing a parameter related to a user, storing descriptive data for multiple identifiers, and indexing multiple events. Each event corresponds to a physical object supplied to a user on behalf of an entity. The method includes identifying a first set of identifiers based on commonality among the descriptive data. The method includes training a machine learning model for the first set of identifiers based on event data from within a predetermined epoch. The method includes receiving an indication of a selected identifier and determining a first intake metric of the selected identifiers using the machine learning model. The method includes determining a second intake metric of the selected identifier and the parameter and transforming the user interface according to the first and second intake metrics. The first intake metric represents an amount of resources expected to be received during a second epoch subsequent to the predetermined epoch.

Abstract: A method for generating predictions for year-over-year change in drug spending and per member per month spending and for providing the predictions via a web portal includes receiving data collected from a health plan. The data includes per member per month costs of the health plan and demographic information of members of the health plan. The method includes selecting therapeutic classes based on the per member per month costs and demographic information, segmenting the data by the therapeutic classes, and detecting patterns by analyzing the segmented data. The method includes generating models for the therapeutic classes based on the patterns, and generating predictions for year-over-year change in drug spending and per member per month spending for the therapeutic classes by utilizing the models. The method includes providing the predictions via a web portal in at least one of a displayable graphical format and a downloadable data structure.

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 generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

Abstract: A system and method of establishing instant communication session with health providers starts with processor receiving request from member-related client device and generating, using a topic model engine, keywords based on reported information associated with user of the member-related client device and historical provider information associated with user. Processor generates risk scores using predictive models associated with conditions and identifies specialists based on the keywords and the risk scores. Processor selects a subset of the specialists based on a user's preferences associated with gender, age, or language and identifies a specialist match based on a highest satisfaction score or a lowest contractual rate. The specialist match is a specialist included in the subset. Processor establishes a communication session between the member-related client device and a provider client device associated with the specialist match. Other embodiments are disclosed herein.

Abstract: A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.

Abstract: A method includes generating an intervention model for a population of users based on contact data, demographic data, and engagement data indicating successfulness of prior interventions for each of the population of users. The method includes, obtaining first data related to a first user, including engagement data indicating successfulness of prior interventions with the first user. The method includes supplying the obtained data as input to the intervention model to determine an intervention expectation, which indicates a likelihood that the first user will take action in response to an intervention. The method includes determining a likelihood of a gap in care. The method includes, in response to the care gap likelihood exceeding a minimum threshold, selecting and scheduling execution of a first intervention. The first intervention is one of a real-time communication with the first user by a specialist and an automated transmission of a message to the first user.

Abstract: A computer-implemented method includes generating an intervention model for a population of users based on engagement data indicating successfulness of prior interventions for each of the population of users. Each prior intervention corresponds to one of multiple engagement channels, and the intervention model includes multiple channel-specific models. The method includes supplying data related to a first user as input to the intervention model to determine multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the first user will take action in response to an intervention being executed using the corresponding engagement channel.

Abstract: A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training a machine learning model to generate an entity expiration likelihood output, obtaining a set of multiple database entities, and processing, by the machine learning model, feature vector inputs associated with each database entry to generate an entity expiration likelihood output. The instructions include determining a subset of the database entities having the highest entity expiration likelihood outputs, and, for each database entity in the subset, determining output impact scores for parameters of the feature vector input associated with the database entity, generating a feature list based on the determined output impact scores, and automatically selecting an executable sequence according to the entity expiration likelihood output associated with the database entity.

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: 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: Data threat evaluation systems and methods are described. A data model structure includes a data subset from the plurality of data types that predate a known threat; this third data subset includes data types from both a first data subset and a second data subset. A model schema extracts, from the 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 models back on the data set to identify potential threats. The system can operate to integrate data to predict fraud, waste or abuse.

Abstract: Systems and methods herein describe a mental health management system. The mental health management system accesses patient data associated with a patient from a database, determines that the patient is associated with a trigger event, identifies a category associated with the trigger event, generates a notification based on the trigger event and the identified category, validates the notification based on a notification history and transmits the notification to a computing device.

Abstract: Systems and methods in this document describe a mental health management system. The mental health management system accesses patient data associated with a patient from a database, determines that the patient is associated with a trigger event, generates a prediction, using a predictive modeling system trained to analyze the patient data, the prediction corresponding to a probability that the patient's current medication data will be modified, stores the prediction in association with the patient data, and transmits the prediction to a computing device.

Abstract: A method includes maintaining a question repository in which each question corresponds to a set of decision trees. A distance matrix encodes a distance between each pair of questions. In response to a request for a new question, the method converts the new question into a set of tokens. For each question of the existing questions, the method determines a minimum distance between each token of the new question and the tokens of the question and sums the minimum distances to calculate a distance between the question and the new question. The method includes performing cluster analysis on the distance matrix. Performing cluster analysis includes normalizing the distance matrix and applying a hierarchical clustering process to the normalized distance matrix. Based on the cluster analysis, the method transmits an alternative question proposal or adds the new question to the question repository.