Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, causal risk-based scores. The techniques may include generating a predictive risk-based opportunity score for an evaluation entity based on (i) a plurality of engagement scores and (ii) a plurality of predictive risk scores respectively corresponding to a plurality of predictive entities within an entity cohort associated with the evaluation entity. Using action-specific causal inference models, a predictive impact score of a prediction-based action on the evaluation entity is generated and used to generate a causal gap closure score for the evaluation entity based on a gap closure rate associated with the evaluation entity. The techniques include generating a causal risk-based impact score for the prediction-based action and the evaluation entity based on the predictive risk-based opportunity score, the predictive impact score, and a predictive improvement measure.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for initiating presentation of an interactive user interface. The techniques may include receiving one or more candidate prediction-based actions and generating a plurality of causal risk-based impact scores with respect to a candidate prediction-based action. The techniques include generating a plurality of causal quality-based impact scores and an action sequence for a plurality of evaluation entities and generating a causal net impact score based on (i) an aggregation of the plurality of causal risk-based impact scores and the plurality of causal quality-based impact scores and (ii) a sequence impact metric corresponding to the action sequence. The techniques include generating a sequence ranking for the action sequence and initiating a presentation of an interactive user interface reflective of the action sequence and the sequence ranking.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, categorical improvement predictions. The techniques may include generating a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group. The techniques include using an action-specific causal inference model to generate a metric-specific predictive impact measure. The techniques include generating a metric-level categorical improvement prediction and a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a multi-context convolutional self-attention machine learning framework that comprises a shared token embedding machine learning model, a plurality of context-specific self-attention machine learning models, and a cross-context representation inference machine learning model, where each context-specific self-attention machine learning model is configured to generate, for each input text token of an input text sequence, a context-specific token representation using a context-specific self-attention mechanism that is associated with the respective distinct context window size for the context-specific self-attention machine learning model.

Abstract: Systems and methods are disclosed for processing forms to automatically adjudicate religious exemptions. The method includes receiving an input from a user to data fields of forms associated with a religious exemption request, wherein the input is in a first data format and includes location information, religious information, employment information, or demographic information associated with the user. Exemption-relevant features are determined from the input. A data object including the exemption-relevant features is generated. The exemption-relevant features are transformed into corresponding embeddings in a second data format, wherein the embeddings represent semantic relations between the exemption-relevant features. The authenticity of the data object is determined based on the embeddings using a classification model that has been trained using a plurality of embeddings representative of a plurality of exemption-relevant features.

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 provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a multi-context convolutional self-attention machine learning framework that comprises a shared token embedding machine learning model, a plurality of context-specific self-attention machine learning models, and a cross-context representation inference machine learning model, where each context-specific self-attention machine learning model is configured to generate, for each input text token of an input text sequence, a context-specific token representation using a context-specific self-attention mechanism that is associated with the respective distinct context window size for the context-specific self-attention machine learning model.

Abstract: A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Abstract: Systems and methods are disclosed for predicting diagnoses in medical records. A method includes receiving one or more documents, wherein the one or more documents include medical records. An optical character recognition (OCR) engine is used to extract text from the one or more documents. A natural language processing (NLP) model is used to determine one or more predictions and attention scores for one or more tokens in the one or more documents, wherein each of the one or more tokens represents a word in the extracted text. The one or more tokens are aggregated based on the one or more attention scores to construct sentences. The constructed sentences are presented to a user via a graphical user interface of a device.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing (NLP) operations on multi-segment documents. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform NLP operations on multi-segment documents by generating document segmentation machine learning models, using document segmentation machine learning models to determine document segments of input multi-segment documents, enabling adaptive multi-segment summarization of multi-segment documents, and enabling guided interaction with multi-segment documents.

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: A method comprises: obtaining a current initial image generated by an image generator of an imaging device based on current input signals of sensors of the imaging device; and applying a transformation model to the current initial image to generate a current transformed image, wherein the transformation model is a machine-learning model that has been trained to generate transformed images that more closely resemble reference images generated by a reference image generator.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a multi-context convolutional self-attention machine learning framework that comprises a shared token embedding machine learning model, a plurality of context-specific self-attention machine learning models, and a cross-context representation inference machine learning model, where each context-specific self-attention machine learning model is configured to generate, for each input text token of an input text sequence, a context-specific token representation using a context-specific self-attention mechanism that is associated with the respective distinct context window size for the context-specific self-attention machine learning model.

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 data. In one example, embodiments comprise receiving a categorical input feature, generating an image representation of the categorical input feature, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing (NLP) operations on multi-segment documents. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform NLP operations on multi-segment documents by generating document segmentation machine learning models, using document segmentation machine learning models to determine document segments of input multi-segment documents, enabling adaptive multi-segment summarization of multi-segment documents, and enabling guided interaction with multi-segment documents.

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 input data entities that describe temporal relationships across a large number of prediction input codes. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using hybrid prediction scores that are determined based at least in part on co-occurrence-based prediction scores and temporal prediction scores, where the co-occurrence-based prediction scores are determined based at least in part on co-occurrence-based historical representation of a sequence of prediction input codes and temporal historical representation of the sequence of prediction input codes.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using at least one of segment-wise feature processing machine learning models or a multi-segment representation machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using at least one of shared segment embedding machine learning models or transformer-based machine learning models.

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 data. In one example, embodiments comprise receiving a categorical input feature, generating an image representation of the categorical input feature, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.