Abstract: Various embodiments of the present disclosure provide production line conformance measurement techniques using intelligent retraining of machine learning models. The techniques may include receiving, using a performance metric event stream associated with a categorical validation ensemble model, a performance metric event associated with a categorical validation machine learning model of the categorical validation ensemble model. In response a determination that the performance metric event satisfies a defined performance metric threshold, the techniques may also include identifying a training dataset for the categorical validation machine learning model and generating, and using the training dataset, an updated version of the categorical validation machine learning model. The training dataset may include a plurality of training production line images each associated with an object identifier, a site identifier, and/or a fill level.

Abstract: Various embodiments of the present disclosure provide image and prediction processing techniques for providing improved image-based prediction. The techniques may include generating a transformed image from a production line image corresponding to a primary orientation and the generating one or more derivative transformed images for the production line image, each corresponding to one of one or more derivative orientations from the primary orientation. The techniques may include generating, using a categorical validation machine learning model, a plurality of validation predictions for the production line image based on the transformed image and the one or more derivative transformed images. The techniques include generating an aggregate validation prediction based on the plurality of validation predictions and initiating the performance of the prediction-based action based on the aggregate validation prediction.

Abstract: Systems and methods are disclosed for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for production line conformance monitoring. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform production line conformance monitoring by utilizing categorical validation machine learning models that are generated using a plurality of training production line images associated with a related category subset of a plurality of validation categories for a target validation category.

Abstract: Systems and methods are disclosed for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score.

Abstract: Systems and methods are disclosed for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score.

Abstract: A computing system may receive a verification of benefits (VoB) request that identifies a subscriber and a healthcare provider that submitted the VoB request. The VoB request may comprise a request for the computing system to verify that a health insurance policy of the subscriber is active. In response to determining that the health insurance policy of the identified subscriber is active, the computing system may determine whether to send a subscriber alert to the subscriber. The subscriber alert may notify the subscriber that the healthcare provider has been requested to perform a healthcare action for the subscriber in the future as of a time the healthcare provider submitted the VoB request. Based on a determination to send the subscriber alert, the computing system may initiate a process to send the subscriber alert.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing demographic-aware federated machine learning. In one example, a method includes receiving local machine learning model data objects from model data object provider agents; for each inference-profile pair that is associated with a corresponding inference identifier and a corresponding model profile, generating a global machine learning model data object based at least in part on a related local model subset of the local machine learning model data objects for the inference-profile pair; and providing, based at least in part on each global machine learning model data object, a demographic-aware predictive data analysis application programming interface (API), wherein the demographic-aware predictive data analysis API is accessible by the model data object provider agents.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for production line conformance monitoring. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform production line conformance monitoring by utilizing categorical validation machine learning models that are generated using a plurality of training production line images associated with a related category subset of a plurality of validation categories for a target validation category.

Abstract: There is a need for solutions that increase efficiency and reliability of medical record processing systems. This need can be addressed, for example, by receiving a free-form primary provider input from a primary provider profile; detecting one or more information deficiencies in the primary provider input, wherein each information deficiency indicates a respective query; for each identified information deficiency of the one or more information deficiencies, identifying a supportive profile associated with the respective information deficiency, causing the supportive profile computing entity associated with the respective supportive provider profile to present an audiovisual interface that includes an indication of the respective query associated with the information deficiency, and receiving a supportive provider input from each supportive provider profile; and generating a collaborative record for the primary provider input based on the primary provider input and each received supportive provider input.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing demographic-aware federated machine learning. In one example, a method includes receiving local machine learning model data objects from model data object provider agents; for each inference-profile pair that is associated with a corresponding inference identifier and a corresponding model profile, generating a global machine learning model data object based at least in part on a related local model subset of the local machine learning model data objects for the inference-profile pair; and providing, based at least in part on each global machine learning model data object, a demographic-aware predictive data analysis application programming interface (API), wherein the demographic-aware predictive data analysis API is accessible by the model data object provider agents.

Abstract: A computing system may receive a verification of benefits (VoB) request that identifies a subscriber and a healthcare provider that submitted the VoB request. The VoB request may comprise a request for the computing system to verify that a health insurance policy of the subscriber is active. In response to determining that the health insurance policy of the identified subscriber is active, the computing system may determine whether to send a subscriber alert to the subscriber. The subscriber alert may notify the subscriber that the healthcare provider has been requested to perform a healthcare action for the subscriber in the future as of a time the healthcare provider submitted the VoB request. Based on a determination to send the subscriber alert, the computing system may initiate a process to send the subscriber alert.

Abstract: There is a need for solutions that increase efficiency and reliability of medical record processing systems. This need can be addressed, for example, by receiving a free-form primary provider input from a primary provider profile; detecting one or more information deficiencies in the primary provider input, wherein each information deficiency indicates a respective query; for each identified information deficiency of the one or more information deficiencies, identifying a supportive profile associated with the respective information deficiency, causing the supportive profile computing entity associated with the respective supportive provider profile to present an audiovisual interface that includes an indication of the respective query associated with the information deficiency, and receiving a supportive provider input from each supportive provider profile; and generating a collaborative record for the primary provider input based on the primary provider input and each received supportive provider input.