Abstract: Various embodiments of the present disclosure provide techniques for improving causal inference modeling with respect to predictive labels in a complex predictive domain. The techniques of the present disclosure may include generating a positive cohort and a negative cohort of data objects from a dataset based on an associated with a predictive label, generating a positive cohort impact measure for the positive cohort and one or more negative cohort impact measures for the negative cohort, and generating an object impact measure for a particular data object of the negative cohort based on the positive cohort impact measure and at least one of the negative cohort impact measures.

Abstract: Various embodiments of the present invention utilize systems, methods, and computer program products that perform measurement device calibration management by utilizing calibration offset generation machine learning models that are generated using a model training routine that comprises, for each measurement environment feature value: (i) determining a plurality of inferred measurements by a measurement device in relation to a ground-truth measurement operation via performing the ground-truth measurement operation under simulated measurement conditions characterized at least in part by varying a measurement environment feature that is associated with the measurement environment feature value across a per-feature spectrum for the measurement environment feature; and (ii) generating the calibration offset generation machine learning model based at least in part on comparing the plurality of inferred measurements and a ground-truth measurement output for the ground-truth measurement operation.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, providing a temporal disease risk profile describing a likelihood of disease onset over time for an individual in a dynamically interpretable manner. Interpretability of the temporal disease risk profile is enabled by providing additional and contextual information, such as weight distributions of various health indicators, factors, and features.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for converting a multilabel classification model into a sequence of a plurality of binary classification models based on a plurality of label subgroups associated with the multilabel classification model, where the label subgroups comprise an optimal subgroup size, the optimal subgroup size is generated by optimizing an optimization measure defined by a subgroup size variable and a total inner group correlation measure, and identifying label membership to a particular subgroup by using a mixed integer linear program model.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include generating a plurality of encoded input data objects associated with a measurement device; generating, using at least a bidirectional Recurrent Neural Networks (RNN) machine learning model, a predictive performance data object associated with the measurement device and a plurality of predictive weight data objects associated with the predictive performance data object, and performing one or more prediction-based actions based at least in part on the predictive performance data object or the plurality of predictive weight data objects.

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. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using a cross-temporal encoding machine learning model, such as a cross-temporal encoding machine learning model that is generated by using a target intervention classification machine learning model to map outputs of the cross-temporal encoding machine learning model to historical target intervention labels, thus enabling supervised training of the cross-temporal encoding machine learning without the need for ground-truth data corresponding to the output of the cross-temporal encoding machine learning model.

Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may include generating a plurality of encoded input data objects associated with a measurement device; generating, using at least a bidirectional Recurrent Neural Networks (RNN) machine learning model, a predictive performance data object associated with the measurement device and a plurality of predictive weight data objects associated with the predictive performance data object, and performing one or more prediction-based actions based at least in part on the predictive performance data object or the plurality of predictive weight data objects.

Abstract: Various embodiments of the present disclosure provide machine learning model-based risk prediction and treatment pathway prioritization for entities associated with a respective disparity group. Example embodiments are configured to generate, using a risk prediction model, an individual risk score for an entity of a disparity group associated with an entity cohort. Example embodiments are also configured to generate, using a disparity risk adjustment model, a disparity adjusted risk score for the entity based on the individual risk score. Example embodiments are also configured to initiate various prediction-based actions for the entity based on a comparison between the disparity adjusted risk score and a risk score threshold. Example embodiments are also configured to generate a phenotypic profile for the entity based on an evaluation data object and an image-based evaluation data object for the entity and generate a prediction-based action sequence for the entity based on the phenotypic profile.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a correlated prediction for an input data record by generating a correlation matrix based on co-occurrences associated with a plurality of reference non-correlated predictions, generating a simulation matrix comprising a plurality of simulation data records based on a number of simulation instances and the plurality of reference non-correlated predictions, generating a plurality of correlated simulation data records based on the correlation matrix and select ones of the plurality of simulation data records, generating one or more univariates based on the plurality of correlated simulation data records, and determining a correlated prediction based on a comparison of the one or more univariates and a plurality of input non-correlated probabilities associated with the input data record.

Abstract: In one aspect, the disclosure relates to compounds that are inhibitors of KRAS, and the disclosed compounds are allosteric inhibitors of KRAS which render them extremely useful for therapeutic intervention in a variety of disorders and diseases in which inhibition of DHODH can be clinically useful, e.g., cancer. In various aspects, the disclosed compounds are substituted 7-(piperazin-1-yl)pyrazolo[1,5-a]pyrimidine analogs. In further aspects, the disclosed compounds can be used in methods of treating a cancer. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.

Abstract: Systems and methods are disclosed for systems and methods for optimizing a sample size of an investigation.

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 disclosure provide techniques for improving causal inference modeling with respect to predictive labels in a complex predictive domain. The techniques of the present disclosure may include generating a positive cohort and a negative cohort of data objects from a dataset based on an associated with a predictive label, generating a positive cohort impact measure for the positive cohort and one or more negative cohort impact measures for the negative cohort, and generating an object impact measure for a particular data object of the negative cohort based on the positive cohort impact measure and at least one of the negative cohort impact measures.

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: Systems and methods are disclosed for allocating system resources. One or more processors may receive a plurality of system data sets and a plurality of user data sets. The one or more processors may determine one or more target systems by applying one or more filters to the plurality of system data sets and one or more target users associated. One or more processors may determine one or more target user data sets associated with each of the one or more target users. One or more processors may apply a machine-learning model to the one or more target user data sets to generate a user-level score. One or more processors may generate a system-level score for each of the one or more target systems associated with the one or more target users. One or more processors may initiate performance of one or more actions.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by generating an optimization function for generating an optimal amount of resources to allocate to data objects in each of one or more data object cohorts based on nonlinear causal effect predictions and generating an optimal parameter occurrence set based on the determined optimal amount of resource. Nonlinear causal effects of selected amounts of type-varied resources assigned to specific data objects are predicted on an outcome of interest associated with the data objects.

Abstract: Systems and methods are disclosed for prioritizing one or more provider for data maintenance. The method includes receiving historical claim information from each of one or more providers. The method includes applying a respective model to the historical claim information received from each of the one or more providers. The method includes determining a respective expected number of claims for each of the one or more providers. The method includes normalizing the respective expected number of claims for each of the one or more providers. The method includes determining a respective claim likelihood score for each of the one or more providers. The method includes ranking one or more providers based on each provider's respective expected number of claims.

Abstract: Various embodiments of the present disclosure provide cohort prediction and activity forecasting techniques for implementing improved population analytics in various prediction domains. The techniques may include generating a documented parameter rate for an entity cohort and a predicted parameter rate for the entity cohort based on a plurality of entity-specific parameter scores. The techniques include generating a predicted documentation error for the entity cohort based on a comparison between the documented parameter rate and the predicted parameter rate and, responsive to the predicted documentation error, initiating, using one or more cohort-specific causal models, the performance of an error correction action for the entity cohort.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by determining an optimal amount of resources to allocate to resource-receiving entities in each of one or more resource-receiving entity cohorts based on non-linear causal effect predictions, and determining an optimum operation configuration based on the determined optimal amount of resource. Non-linear causal effect of selected amounts of resources assigned to specific resource-receiving entities are predicted on an outcome of interest associated with the resource-receiving entities.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for allocating resources. The method comprises receiving, by a computing device using a resource allocation machine learning framework, historical data comprising one or more causal variables corresponding to one or more actions or inactions with respect to one or more resource-requesting entities and one or more outcomes of one or more actions, identifying, by the computing device, given ones of one or more resource-requesting entity subgroups based at least in part on the one or more causal effect predictions, and performing, by the computing device, one or more prediction-based actions based at least in part on the identification of the given one or more resource-requesting entity subgroups.