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: 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: 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: Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations using a hierarchical risk prediction machine learning framework that comprises an initially-deployed risk prediction machine learning model, a dynamically-deployed risk prediction machine learning model, and a risk aggregation machine learning model. In some embodiments, the dynamically-deployed risk prediction machine learning model is deployed when a dynamic deployment training entry count of one or more dynamic deployment training entries satisfies a dynamic deployment training entry count threshold.

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: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis on document data objects that are associated with an ontology graph. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations on document data objects that are associated with an ontology graph using document embeddings that are generated by graph-embedding-based paragraph vector 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.

Abstract: The disclosed device, systems and methods relate to a novel catheter, system and methods. Exemplary embodiments comprise a plurality of lumens and balloons for insertion into the aorta and vena cava. These catheters are for use in cardiopulmonary resuscitation and other medical or surgical conditions that require emergency restoration of cerebral and cardiac blood supply.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using affirmative fingerprint distance measures and negative fingerprint distance measures.

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: 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: 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: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis on document data objects that are associated with an ontology graph. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations on document data objects that are associated with an ontology graph using document embeddings that are generated by graph-embedding-based paragraph vector machine learning models.

Abstract: The disclosed device, systems and methods relate to a novel catheter, system and methods. Exemplary embodiments comprise a plurality of lumens and balloons for insertion into the aorta and vena cava.

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: 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: 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 genetic variant data. In one example, embodiments comprise receiving an input feature, generating one or more image representations of the input feature, generating a tensor representation of the one or more image representations, generating a plurality of positional encoding maps, 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.