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 mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.

Abstract: As described herein, various embodiments of the present invention disclose techniques that improve efficiency of performing image-based machine learning operations on large images while limiting accuracy drawbacks of partial processing of those large images by using composite-tiled image embeddings for composite-tiled images generated by merging multiple tiled images that are generated using multiple tiling mechanisms. For example, in some embodiments, given an input image that comprises R image regions, each tiled image comprises N selected image regions of the R image regions that are selected in accordance with a tiling mechanism (where N<R). In this way, given T tiling mechansisms, T tiled images are generated, and the T tiled images are merged to generate a composite-tiled image. Accordingly, by using T tiling mechansisms, various embodiments enable reducing the size of feature data provided to an image processing machine learning model by selecting non-holistic subsets of an input image.

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 mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: A system for predicting multiple-sequential-event based outcomes may include: a first deep neural network configured to predict a first decision, and that includes: a first input layer configured to receive as input less than an entirety of a plurality of variables; and an internal layer configured to receive as input a remainder of the plurality of variables appended to an output of a preceding layer, such that the first deep neural network is configured generate a prediction value for the first decision; and a second deep neural network configured to predict a second decision subsequent to the first decision, and that includes: a second input layer configured to receive as input a representation of output from a penultimate layer appended to at least a portion of the plurality of variables, wherein the second deep neural network is configured to generate a prediction value for the second decision.

Abstract: Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.

Abstract: Systems and methods are disclosed for processing medical claims to determine vector embeddings for predictive analytics and fraud detection. The method includes receiving claim data for a medical claim, the claim data comprising a plurality of medical codes. A plurality of embedding vectors is determined based on the plurality of medical codes, each embedding vector determined based on a corresponding medical code. A multi-layered matrix is determined based on the plurality of embedding vectors determined for the plurality of medical codes, wherein the multi-layered matrix is representative of the medical claim. One or more actions such as predictive analytics and fraud detection are performed based on the multi-layered matrix.

Abstract: Systems and methods are disclosed for processing medical claims to determine vector embeddings for predictive analytics and fraud detection. The method includes receiving claim data for a medical claim, the claim data comprising a plurality of medical codes. A plurality of embedding vectors is determined based on the plurality of medical codes, each embedding vector determined based on a corresponding medical code. A multi-layered matrix is determined based on the plurality of embedding vectors determined for the plurality of medical codes, wherein the multi-layered matrix is representative of the medical claim. One or more actions such as predictive analytics and fraud detection are performed based on the multi-layered matrix.

Abstract: As described herein, various embodiments of the present invention disclose techniques that improve efficiency of performing image-based machine learning operations on large images while limiting accuracy drawbacks of partial processing of those large images by using composite-tiled image embeddings for composite-tiled images generated by merging multiple tiled images that are generated using multiple tiling mechanisms. For example, in some embodiments, given an input image that comprises R image regions, each tiled image comprises N selected image regions of the R image regions that are selected in accordance with a tiling mechanism (where N<R). In this way, given T tiling mechansisms, T tiled images are generated, and the T tiled images are merged to generate a composite-tiled image. Accordingly, by using T tiling mechansisms, various embodiments enable reducing the size of feature data provided to an image processing machine learning model by selecting non-holistic subsets of an input image.

Abstract: Systems and methods are configured to extract images from provided source data files and to preprocess such images for content-based image analysis. An image analysis system applies one or more machine-learning based models for identifying specific features within analyzed images, and for determining one or more measurements based at least in part on the identified features. Such measurements may be embodied as absolute measurements for determining an absolute distance between features, or relative measurements for determining a relative relationship between features. The determined measurements are input into one or more machine-learning based models for determining a classification for the image.

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 cross-geographical predictive data analysis that enhance network transmission efficiency. In one example, a method includes determining forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period; determining forecasted inferior domain event data for each hierarchically inferior geographic domain associated with the hierarchically superior geographic domain at the forecasting period; determining confirmed inferior domain event data based at least in part on each hierarchically inferior geographic domain; and performing prediction-based actions based at least in part on each confirmed inferior domain event data.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of techniques using time bound code transition likelihood data objects, techniques using cross-code relationship values, techniques using augmented entity-code occurrence data objects, techniques using per-pathway text representations of inferred occurrence pathways of a one or more individual historic code occurrences, techniques using polygenic risk score (PRS) measures, and/or the like.

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 cross-geographical predictive data analysis that enhance network transmission efficiency. In one example, a method includes determining forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period; determining forecasted inferior domain event data for each hierarchically inferior geographic domain associated with the hierarchically superior geographic domain at the forecasting period; determining confirmed inferior domain event data based at least in part on each hierarchically inferior geographic domain; and performing prediction-based actions based at least in part on each confirmed inferior domain event data.

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 mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: Systems and methods are configured to extract images from provided source data files and to preprocess such images for content-based image analysis. An image analysis system applies one or more machine-learning based models for identifying specific features within analyzed images, and for determining one or more measurements based at least in part on the identified features. Such measurements may be embodied as absolute measurements for determining an absolute distance between features, or relative measurements for determining a relative relationship between features. The determined measurements are input into one or more machine-learning based models for determining a classification for the image.

Abstract: Systems and methods are configured to extract images from provided source data files and to preprocess such images for content-based image analysis. An image analysis system applies one or more machine-learning based models for identifying specific features within analyzed images, and for determining one or more measurements based at least in part on the identified features. Such measurements may be embodied as absolute measurements for determining an absolute distance between features, or relative measurements for determining a relative relationship between features. The determined measurements are input into one or more machine-learning based models for determining a classification for the image.

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 cross-geographical predictive data analysis that enhance network transmission efficiency. In one example, a method includes determining forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period; determining forecasted inferior domain event data for each hierarchically inferior geographic domain associated with the hierarchically superior geographic domain at the forecasting period; determining confirmed inferior domain event data based at least in part on each hierarchically inferior geographic domain; and performing prediction-based actions based at least in part on each confirmed inferior domain event data.

Abstract: Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.