Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes generating, by a processor, utilizing a risk determination machine learning model and based at least in part on one or more hidden features of the first predictive entity, the predicted risk measure, and performing one or more prediction-based actions based at least in part on the predicted risk measure.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.

Abstract: Instances of claim data are received by an apparatus. Each instance of claim data corresponds to an entity. A claim vector is generated for each instance of claim data. The claim vector is added to a group of claim vectors that all correspond to a same entity. Each group of claim vectors is aggregated to generate an entity vector corresponding to the entity. Based at least in part on the entity vector and (a) an entity profile corresponding to the corresponding entity or (b) an entity cluster with which the entity is associated, at least one behavior signal value is determined for the entity. A behavior signal is amended to include the behavior signal value. The behavior signal comprises at least two behavior signal values. Each of the behavior signal values are associated with a different time period. The behavior signal is provided for display and/or further processing.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically generating multi-paradigm feature representations are provided. An example method may include generating a code dataset including a plurality of codes associated with a predictive entity; generating a plurality of semantic feature vectors based at least in part on code description metadata; generating a plurality of structural feature vectors based at least in part on code relation metadata; generating a plurality of multi-paradigm feature vectors based at least in part on the plurality of semantic feature vectors and the plurality of structural feature vectors; generating a prediction for the predictive entity by processing the plurality of multi-paradigm feature vectors using a prediction model; and performing one or more prediction-based actions based on the prediction.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying one or more visual impairments of a user, mapping the visual impairments to one or more accessibility solutions, (e.g., program code entries) and dynamically modifying a display presentation based at least in part on the identified accessibility solutions.

Abstract: In general, this disclosure describes techniques for automatically restructuring a database to improve one or more parameters of the database. In some examples, a computing system is configured to extract a set of columns and merge the set of columns into a new table of a first new candidate model of the database; determine a table of the database based on a number of columns of that are involved in query “where” or “join” clauses; merge the table with one or more connected tables in a second new candidate model of the database, wherein the one or more connected tables are connected to the table by at least one of the “where” or “join” clauses; select a model of the database from among the candidate models based on one or more parameters; and use the selected model as the current model of the database.

Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. In one embodiment, this need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and/or unstructured fusion machine learning. In one particular example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing cross-temporal predictive data analysis. In one example, a method includes determining a time-adjusted encoding for each temporal unit of a group of temporal units, processing each time-adjusted encoding using a cross-temporal encoding machine learning model to generate a cross-temporal encoding of the group of temporal units, and performing one or more prediction-based actions based at least in part on the cross-temporal encoding.

Abstract: A computing device includes a memory and processing circuitry. The memory is configured to store an organizational proximity dataset for a current user. The processing is configured to generate scores for a plurality of resources based on the organizational proximity dataset stored to the memory for the current user. The processing circuitry is further configured to recommend one or more resources of the plurality of resources to the current user based on the scores generated for the plurality of resources.

Abstract: There is a need for more effective and efficient document processing solution. Accordingly, various embodiments of the present invention introduce various document processing optimization solutions. In one example, a method includes identifying a plurality of input pages each associated with a related input document of a plurality of input documents; for each input page of the plurality of input pages, generating a segmented page; processing each segmented page using a trained encoder model to generate a fixed-dimensional representation of the input page; determining, based at least in part on each fixed-dimensional representation, a plurality of document clusters; determining a plurality of processing groups, where each processing group is associated with one or more related document clusters of the plurality of document clusters; and performing the document processing optimization based at least in part on the plurality of processing groups.

Abstract: Various embodiments of the present disclosure are directed to model feature classification, analysis, and updating for analyzing one or more risk determination model(s). Embodiments include an improved apparatus configured to generate an actionable feature data object subset of a risk model feature set for a risk determination model, for example using an actionable determination model. The apparatus may provide the actionable feature data object subset for rendering to an interface of a client device. The apparatus may additionally or alternatively be configured to utilize user feedback, provided either directly or identified by the apparatus based on user interactions, to update the actionable determination model. The apparatus may additionally or alternatively be configured to maintain and utilize linked claim data object(s) for use in rendering a linked claim scores analysis interface that provides additional insight regarding the risk level of a particular entity.

Abstract: There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically prioritizing a data processing queue are provided. An example method may include retrieving a plurality of data objects in the data processing queue, generating a base data table based at least in part on the plurality of data objects, determining a predictive data model based at least in part on the base data table, and adjusting a queue order of the plurality of data objects in the data processing queue based at least in part on a risk score calculated by the predictive data model.

Abstract: There is a need to accurately and dynamically generate a time-based prediction and control access to the time-based prediction. This need can be addressed, for example, by allowing controlled access to user information pertaining to a user visiting a facility. In one example, a method includes storing a companion user identifier in association with a public user identifier; receiving a request for a user status of the user; determining whether the companion user is permitted to receive the user status of the user based at least in part on the public user identifier and the companion user identifier; responsive to determining that the companion user is permitted to receive the user status of the user, requesting an aggregated time-based prediction for the user; and causing, the aggregated time-based prediction to be decrypted and provided as a response to the request for the user status of the user.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using at least one of query-compliant hash databases, segmentation-based hashing models, and hash segmentation models.