Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to categorical data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to categorical data objects by utilizing at least one of predictive feature hierarchies, feature refinement routines, decision subsets of predictive features that are generated based at least in part on predictiveness measures for the predictive features, and/or the like.

Abstract: Various embodiments of the present disclosure provide techniques for generating an ordered code sequence. For example, the techniques may include generating a predictive group code and an anchor code for an entity based on entity data. The techniques may include generating, using the first portion of the composite machine learning model, an unordered code sequence comprising one or more predicted codes based on the entity data, the predictive group code, and the anchor code. The techniques may include generating, using a second portion of the composite machine learning model, an ordered code sequence based on the unordered code sequence. The techniques may include providing the ordered code sequence.

Abstract: Embodiments of the present disclosure provide for improved data imputation and use of imputed data in processing of downstream models. Some embodiments specially train a model that performs improved data imputation utilizing a specially-configured attention mechanism. Some embodiments train a model utilizing stratified masking. Some embodiments train a particular pre-processing layer of a downstream task-specific model to adaptively learn threshold values for imputing particular data. The pre-processing layer is usable to improve accuracy training and/or use of a downstream task-specific model based at least in part on the imputed data.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a plurality of training embeddings based on a pre-training dataset, wherein the plurality of training embeddings comprises one or more of descriptive embeddings, sequential ordering embeddings, age/time embeddings, locale embeddings, or encounter number embeddings; generating one or more initialized weights associated with respective one or more layers of a machine learning model based on the plurality of training embeddings; generating one or more fine-tuned weights for the machine learning model by updating at least a portion of the one or more initialized weights using a fine-tuning dataset associated with a target classification; and generating, using the machine learning model, one or more prediction scores for one or more prediction encounter data elements associated with the target classification, based on one or more input temporal sequence of encount

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis using semi-structured input data. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis using semi-structured input data using at least one of techniques using inferred codified fields and temporally-arranged codified fields.

Abstract: Various embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using an agent machine learning model to determine an optimal clinical intervention based at least in part on the current clinical state and an inferred reinforcement learning policy that is determined based at least in part on a familiarity-adjusted reward function, where the familiarity-adjusted reward function is generated by an environment machine learning framework based at least in part on one or more next state predictions for one or more pruned action-state combinations based at least in part on a historical clinical outcome database, and the one or more pruned action-state combinations are determined based at least in part on one or more pruned clinical actions that are selected from a plurality of candidate clinical actions based at least in part on one or more action pruning criteria.

Abstract: Various embodiments of the present invention introduce technical advantages related to computational efficiency and storage efficiency of training reinforcement learning models using model-based reinforcement learning approaches. For example, various embodiments of the present invention enable training components of a dynamics model of a reinforcement learning framework using cross-space likelihood similarity measures between predicted transition likelihood models and empirical transition likelihood models even when the two noted likelihood models have distinct distribution supports. This enables using training/empirical observation data to train dynamics model components even when the output state spaces of the dynamics model components are distinct from the output state space of the empirical distributions determined using the training/empirical observation data.

Abstract: Various embodiments of the disclosure provide apparatuses, systems, and computer program products for predictive data labelling using a dual-model system. Embodiments provide various advantages in accuracy of predicted labels, for example in various contexts such as medical data analysis for difficult to diagnose diseases.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a prediction output comprising one or more actions by receiving data associated with encounters in a tuple form, tokenizing the encounters, training a causal transformer machine learning model configured to predict outcomes of actions by translating action tokens from the tokenized encounters into one or more embedding spaces, and training a causal transformer machine learning model to select the one or more actions based on embeddings from the one or more embedding spaces.

Abstract: Various embodiments of the present invention introduce technical advantages related to computational efficiency and storage efficiency of training reinforcement learning models using model-based reinforcement learning approaches. For example, various embodiments of the present invention enable training components of a dynamics model of a reinforcement learning framework using cross-space likelihood similarity measures between predicted transition likelihood models and empirical transition likelihood models even when the two noted likelihood models have distinct distribution supports. This enables using training/empirical observation data to train dynamics model components even when the output state spaces of the dynamics model components are distinct from the output state space of the empirical distributions determined using the training/empirical observation data.

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 hybrid input predictive data analysis.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis using semi-structured input data. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis using semi-structured input data using at least one of techniques using inferred codified fields and temporally-arranged codified fields.

Abstract: Various embodiments of the present invention improve the speed of training classification-based machine learning models by introducing techniques that enable efficient parallelization of such training routines while enhancing the accuracy of each parallel implementation of a training routine. For example, in some embodiments, a classification-based machine learning model is trained via executing N parallel processes each executing a portion of a training routine, where each parallel process is performed using a training set having a uniform distribution of labels associated with the classification-based machine learning model. In this way, each parallel process is more likely to update parameters of the classification-based machine learning model in accordance with a holistic representation of the training data, which in turn improves the overall accuracy of the resulting trained classification-based machine learning models while enabling parallel training of the classification-based machine learning model.

Abstract: There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy.

Abstract: There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy.

Abstract: There is a need for solutions that perform predictive natural language processing with improved efficiency and/or accuracy.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include generating edges connecting attribute vertices to a member vertex in a healthcare graph data object, determining, using at least one graph-based machine learning model and based at least in part on the attribute vertices, historical member vertices from the healthcare graph data object, determining, using the at least one graph-based machine learning model and based at least in part on the historical member vertices, historical member query vertices from the healthcare graph data object, generating, based at least in part on the historical member query vertices, predicted member query vertices in the healthcare graph data object, and performing one or more prediction-based actions based at least in part on the one or more predicted member query vertices.

Abstract: Various embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using an agent machine learning model to determine an optimal clinical intervention based at least in part on the current clinical state and an inferred reinforcement learning policy that is determined based at least in part on a familiarity-adjusted reward function, where the familiarity-adjusted reward function is generated by an environment machine learning framework based at least in part on one or more next state predictions for one or more pruned action-state combinations based at least in part on a historical clinical outcome database, and the one or more pruned action-state combinations are determined based at least in part on one or more pruned clinical actions that are selected from a plurality of candidate clinical actions based at least in part on one or more action pruning criteria.

Abstract: There is a need for more accurate and more efficient optimized scheduling operations. This need can be addressed by, for example, techniques for performing one or more optimized scheduling operations. In one example, a method includes: determining, using an optimal event time prediction learning machine model, a predicted interactivity measure for an event data object; determining, based at least in part on the predicted interactivity measure and using an optimal event time prediction machine learning model, an optimal event time modification value for the event data object; and determining, by one or more processors, an optimized appointment prediction based at least in part on optimal event time modification value.

Abstract: Various embodiments of the disclosure provide apparatuses, systems, and computer program products for predictive data labelling using a dual-model system. Embodiments provide various advantages in accuracy of predicted labels, for example in various contexts such as medical data analysis for difficult to diagnose diseases.