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: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.

Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.

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

Abstract: Aggregate vectors corresponding to non-textual information/data are provided in a multi-dimensional space. A computing entity access a plurality of instances of medical information comprising medical codes. The computing entity generates one or more medical sentences from the plurality of instances of medical information. Each medical sentence comprises one or more medical codes. The computing entity generates an embedding vector dictionary comprising a plurality of multi-dimensional vectors based on a medical embedding model trained using machine learning and the one or more medical sentences. Each multi-dimensional vector corresponds to a medical code. The computing entity generates a plurality of aggregate vectors based on the embedding vector dictionary and analyzes at least a portion of the plurality of aggregate vectors to identify two or more aggregate vectors that are similar or different based on a distance between the two or more aggregate vectors in the multi-dimensional space.

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

Abstract: Efficient SQL based multi-attribute clustering of data attributes may be used to identify the most relevant combination of data attributes to an outcome. A global outcome value may be calculated to represent an average of the outcome. A subset outcome value for each subset of data attributes of a plurality of attributes may be calculated to represent average of the outcome for the subset. For each subset of data attributes, a number of members associated with the subset may be compared to a threshold, and the subsets with less members than the threshold may be removed. The subset outcome value for each subset of data attributes may be compared to the global outcome value, and a report may be generated that identifies each subset for which the corresponding subset outcome value is greater than or less than the global outcome value.

Abstract: Efficient SQL based multi-attribute clustering of data attributes may be used to identify the most relevant combination of data attributes to an outcome. A global outcome value may be calculated to represent an average of the outcome. A subset outcome value for each subset of data attributes of a plurality of attributes may be calculated to represent average of the outcome for the subset. For each subset of data attributes, a number of members associated with the subset may be compared to a threshold, and the subsets with less members than the threshold may be removed. The subset outcome value for each subset of data attributes may be compared to the global outcome value, and a report may be generated that identifies each subset for which the corresponding subset outcome value is greater than or less than the global outcome value.

Abstract: Methods, systems, and apparatuses for comparing healthcare data are disclosed. Some embodiments of the method for comparing healthcare data may include receiving healthcare data for geographic regions. The healthcare data may include healthcare variables and clinical data. Some embodiments of the method may further include analyzing, with a processing device, one or more healthcare variables for geographic regions. In some embodiments of the method, analyzing the one or more healthcare variables may include assigning each geographic region to one of several clusters based on the one or more healthcare variables. Some embodiments of the method may further include analyzing, with a processing device, the clinical data for geographic regions. In some embodiments of the method, analyzing the clinical data may include determining a primary magnitude for each geographic region of a primary measure, determining a secondary magnitude for each geographic region of a secondary measure, and comparing them.

Abstract: System and methods for processing price data for health services are provided. In one embodiment, the system includes a data storage device configured to store a database comprising one or more records. The system may also include a server in data communication with the data storage device. The server may be suitably programmed to search the database to obtain a group of records of prices for a health service received by a group of subjects, wherein the health service is comprised in a service category; extract a representative price for the health service; determine a weight for the health service relative to the service category; and generate, using a processor, a weighted price for the health service based on the weight and the representative price.