Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing optimized equipment allocation. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform optimized equipment allocation using at least one of equipment evaluation scoring machine learning models, equipment optimized allocation machine learning models, and equipment history events.

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 performing 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 prospective coverage score determination machine learning models and prospective event-based classification machine learning models.

Abstract: There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving relevant items for user queries by generating, using a search engine machine learning model, a prediction-based action for the query input wherein query input embeddings of the query input are generated. For each query input embedding, a k-Nearest-Neighbor (KNN) search is performed with respect to search engine repository item embeddings to generate initial search results, and for each initial set result, performing N hops within a semantic graph starting from nodes associated with the initial search result to generate related search results. The search engine machine learning model is trained by generating a search engine repository item embeddings according to embedding techniques for respective content categories and generating the semantic graph based at least in part on a measure of similarity for pairs of search engine repository item embeddings.

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: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations using distributed database update management techniques that utilize at least one of a field value temporal scoring machine learning model, total field utility measures, and distributed database update routines.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for perform predictive data analysis operations using natural language input data. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using sentence embedding machine learning models that are trained in coordination with similarity-based machine learning models.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include retrieving a plurality of natural language data objects from a database; determining, based at least in part on the plurality of natural language data objects and by utilizing an entity extraction machine learning model, a plurality of entity identifiers for the plurality of natural language data objects; determining, based at least in part on the plurality of entity identifiers and by utilizing the entity extraction machine learning model, one or more entity relationship identifiers for the plurality of natural language data objects; generating, based at least in part on the plurality of entity identifiers and the one or more entity relationship identifiers, a graph-based data object for the plurality of natural language data objects; and performing one or more prediction based actions based at least in part on the graph-based data object.

Abstract: In general, embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing scalable dynamic data transmissions. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform scalable dynamic data transmission using structured data responses that include structured response extensions, where the structured response extensions may include dynamic extension response fields generated based on a structured request header of a corresponding structured data request.

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: An example method includes obtaining, by a computing system, audio data representing sounds generated by a user; applying, by the computing system, a machine learning (ML) model that generates output data based on non-semantic content of the audio data; and generating, by the computing system, based on the output data generated by the ML model, a recommended healthcare provider list for the user, the recommended healthcare provider list for the user including one or more healthcare providers recommended for the user.

Abstract: Systems and computer-implemented method for evaluating programs are disclosed. A computer-implemented method includes determining a propensity score, using a propensity score model, for each patient among multiple patients. The multiple patients include treatment patients and control patients, and the propensity score represents a probability of assignment to a treatment group. The method includes assigning a random value to each patient in an assignment group. The assignment group includes at least one of the treatment patients or the control patients. The method includes sorting the patients based on the assigned random values and matching, based on the sorted patients and the determined propensity scores, each treatment patient to a control patient to create multiple matches. Each match includes one treatment patient and at least one control patient. The method includes performing, based on the multiple matches, one or more actions related to the multiple patients.

Abstract: Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store.

Abstract: There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system. Certain embodiments utilize systems, methods, and computer program products that perform predictive data analysis by utilizing at least one of glucose surge excursion detections, steady-state glucose-insulin machine learning models, and parameter space refinement machine learning models.

Abstract: Systems and methods are disclosed for comparing a plurality of models. The method includes generating raw scores for the plurality of models based on multiple measures of demographic bias and performance. The raw scores for each of the plurality of models are stored in corresponding locations of a raw score matrix. The rank scores for the plurality of models are determined based on comparing the raw scores of the plurality models in each of the multiple measures of demographic bias and performance. The rank scores for each of the plurality of models are stored in corresponding locations of a rank matrix. Tournament scores for the plurality of models are determined based on performing a pairwise comparison of the rank scores. The tournament scores are stored in corresponding locations of a tournament matrix. The tournament scores are tallied to determine a rank for each of the plurality of models.

Abstract: There is a need to dynamically determine one or more levels of difficulty for a computer program associated with one or more levels of difficulty for a computer program associated with one or more dynamic difficulty tasks. In one example, embodiments comprise, during a user interaction session with a computer program, receiving a physiological evaluation data object. One or more physiological difficulty levels may be determined based at least in part on the physiological evaluation data object. One or more program control parameter values for one or more program control parameters may be determined based at least in part on the one or more physiological difficulty levels. User interface data for the computer program may be generated to enable rendering of an in-program experience of the computer program as adjusted by the one or more program control parameter values.

Abstract: Systems and methods for dynamically generating a predicted similarity score for a pair of input sequences. A predicted similarity score for a pair of input sequences is determined based at least in part on at least one of a token-level similarity probability score for the pair of input sequences, a target region match indication for the pair of input sequences, a fuzzy match score for the pair of input sequences, a character-level match score for the pair of input sequences, one or more similarity ratio occurrence indicators for the pair of input sequences, and a harmonic mean score of the fuzzy match score for the pair of input sequences and the token-level similarity probability score for the pair of input sequences.