Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associate

Abstract: A data processing system for machine-learning driven data analysis and reminders implements obtaining an electronic copy of demographic information and an electronic copy of insurance and benefits information associated with a user; providing the demographic information and the insurance and benefits information to a first machine learning model; analyzing the demographic information and the insurance and benefits information with the first machine learning model to output a first benefits utilization prediction that the one or more benefits are available to the user; providing the first benefits utilization prediction as an input to a recommendation engine; generating, using the recommendation engine, a benefits usage summary recommendation report that presents the information regarding the one or more benefits available to the user based on the first benefits utilization prediction; and causing a user interface of a display of a computing device to present the benefits usage summary recommendation report.

Abstract: A data processing system for machine-learning driven price guidance implements obtaining location information indicative of a location associated with a user; obtaining prescription information for a first prescription and first cost prescription; obtaining, from one or more pharmacy benefits managers, prescription cost information for a prescription from a plurality of pharmacies; analyzing the prescription cost information, the location associated with the user, and the prescription information using a machine learning model to obtain a prescription cost information prediction indicating that a first subset of the plurality of pharmacies provide the prescription at a second prescription cost lower than the first prescription cost; providing the prescription cost information prediction to a pharmacy recommendation unit as an input; generating a prescription savings opportunity report that presents the prescription cost information; and causing a user interface of a display of a computing device to present th

Abstract: A data processing system for machine-learning driven data analysis and reminders implements obtaining an electronic copy of demographic information and an electronic copy of insurance and benefits information associated with a user; providing the demographic information and the insurance and benefits information to a first machine learning model; analyzing the demographic information and the insurance and benefits information with the first machine learning model to output a first benefits utilization prediction that the one or more benefits are available to the user; providing the first benefits utilization prediction as an input to a recommendation engine; generating, using the recommendation engine, a benefits usage summary recommendation report that presents the information regarding the one or more benefits available to the user based on the first benefits utilization prediction; and causing a user interface of a display of a computing device to present the benefits usage summary recommendation report.

Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associate

Abstract: A data processing system for machine-learning driven price guidance implements obtaining location information indicative of a location associated with a user; obtaining prescription information for a first prescription and first cost prescription; obtaining, from one or more pharmacy benefits managers, prescription cost information for a prescription from a plurality of pharmacies; analyzing the prescription cost information, the location associated with the user, and the prescription information using a machine learning model to obtain a prescription cost information prediction indicating that a first subset of the plurality of pharmacies provide the prescription at a second prescription cost lower than the first prescription cost; providing the prescription cost information prediction to a pharmacy recommendation unit as an input; generating a prescription savings opportunity report that presents the prescription cost information; and causing a user interface of a display of a computing device to present th

Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associate

Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associate

Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associate

Abstract: A data processing system for machine-learning driven price guidance implements obtaining location information indicative of a location associated with a user; obtaining prescription information for a first prescription and first cost prescription; obtaining, from one or more pharmacy benefits managers, prescription cost information for a prescription from a plurality of pharmacies; analyzing the prescription cost information, the location associated with the user, and the prescription information using a machine learning model to obtain a prescription cost information prediction indicating that a first subset of the plurality of pharmacies provide the prescription at a second prescription cost lower than the first prescription cost; providing the prescription cost information prediction to a pharmacy recommendation unit as an input; generating a prescription savings opportunity report that presents the prescription cost information; and causing a user interface of a display of a computing device to present th

Abstract: A data processing system for machine-learning driven data analysis and reminders implements obtaining an electronic copy of demographic information and an electronic copy of insurance and benefits information associated with a user; providing the demographic information and the insurance and benefits information to a first machine learning model; analyzing the demographic information and the insurance and benefits information with the first machine learning model to output a first benefits utilization prediction that the one or more benefits are available to the user; providing the first benefits utilization prediction as an input to a recommendation engine; generating, using the recommendation engine, a benefits usage summary recommendation report that presents the information regarding the one or more benefits available to the user based on the first benefits utilization prediction; and causing a user interface of a display of a computing device to present the benefits usage summary recommendation report.

Abstract: Exemplary systems and methods to extract, transform, and save to memory features from a training and a test dataset at extraction layers in a machine-learning model. For each data element in the training dataset, at each extraction layer: feature maps are created and grouped by k unique data labels to construct a set of k class-conditional distributions. For each data element in the datasets: distance sets between each feature map of each extraction layer and the extraction layer's class-conditional distributions are calculated and reduced to distance summary metrics. A drift test statistic for each extraction layer is computed between the datasets by comparing the extraction layer's distance summary metric distributions of the test dataset to distance summary metric distributions of the training dataset. The measure of drift between the datasets is computed by combining the test statistics of the extraction layers through a mathematical transform.

Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with an insured user, analyzing the insurance claim information using a first machine learning model to identify an occurrence of a first type of event and a first group of insurance claims associated with the first type of event; analyzing the first group of insurance claims, the policy information, and an indication of the first type of event using a second machine learning model to obtain a prediction whether a respective insurance policy will cover the first insurance claim; and responsive to determining that the respective insurance policy will cover the first insurance claim, causing a user interface to be presented on a display of a computing device associated with the insured user to guide the insured user through submitting the first insurance claim to an insurance provider associated with the

Abstract: The present invention relates to internalizing peptides which facilitate the uptake and transport of cargo into the cytoplasm and nuclei of cells as well as methods for the identification of such peptides. The internalizing peptides of the present invention are selected for their ability to efficiently internalize cargo into a wide variety of cell types both in vivo and in vitro. The method for identification of the internalizing peptides of the present invention comprises incubating a target cell with a peptide display library, isolating peptides with internalization characteristics and determining the ability of said peptide to internalize cargo into a cell.

Abstract: The invention provides a vector, preferably a herpes simplex virus (HSV) vector, comprising a polynucleotide sequence encoding a glutamic acid decarboxylase (GAD) protein. The invention also provides a stock of such vectors and a pharmaceutical composition comprising such vectors. The invention further provides a method of treating pain, such as spinal cord injury pain, in a mammal comprising administering to a mammal a vector comprising a nucleotide sequence encoding a glutamic acid decarboxylase (GAD) protein in an amount effective to treat spinal cord injury pain.

Abstract: The invention provides a herpes simplex virus vector comprising deletions in ICP4, ICP27, and UL55.

Abstract: The invention provides an expression cassette comprising a DNA sequence encoding amino acids 1-99 of human preproenkephalin, a DNA sequence encoding a precursor of a carboxy-amidated peptide flanked by dibasic cleavage sites and optionally a DNA sequence encoding a marker protein (such as Enhanced Green Fluorescent Protein (GFP)) all in operable linkage and under control of a promoter. Where the encoded precursor of a carboxy-amidated peptide is an agonist for an opioid receptor, the invention further provides a method of treating neuropathic pain by administering the gene transfer vector comprising such an expression cassette to a patient.

Abstract: The invention provides an assay for agonists and antagonists of ion channels and for regulators of genetic expression.

Abstract: The present invention relates to internalizing peptides which facilitate the uptake and transport of cargo into the cytoplasm and nuclei of cells as well as methods for the identification of such peptides. The internalizing peptides of the present invention are selected for their ability to efficiently internalize cargo into a wide variety of cell types both in vivo and in vitro. The method for identification of the internalizing peptides of the present invention comprises incubating a target cell with a peptide display library, isolating peptides with internalization characteristics and determining the ability of said peptide to internalize cargo into a cell.