This disclosure relates to method and system for providing user-specific explanations for an output generated by an Artificial Neural Network (ANN) model. The method may include receiving a training dataset, and identifying one or more relevant features from the training dataset. The method may further include distributing the one or more relevant features into a plurality of groups. The plurality of groups may correspond to a plurality of levels of domain knowledge of users. The method may further include generating a plurality of vocabularies of explanations for an output generated by the ANN model for the training dataset corresponding to the plurality of groups, using the one or more relevant features.

A system and computer-implemented method for detecting and addressing billing fraud by an asserted provider of a good or service based on location data associated with card-based financial transactions by an asserted recipient and collected, stored, analyzed, and acted on by a payment card network to determine and respond to a likelihood of fraud. A computer stores locations of the recipient based on uses of a payment card over a time period, and determines a location of the provider based on location data associated with a card reader at a particular time at which the good or service was allegedly provided. The computer compares the two locations for the particular time and generates a fraud score based on a likelihood that the recipient was physically capable of being at the provider location at the particular time, and based on the score, may notify the recipient and decline the payment request.

A system and computer-implemented method for detecting and addressing billing fraud by an asserted provider of a good or service based on location data associated with card-based financial transactions by an asserted recipient and collected, stored, analyzed, and acted on by a payment card network to determine and respond to a likelihood of fraud. A computer stores locations of the recipient based on uses of a payment card over a time period, and determines a location of the provider based on location data associated with a card reader at a particular time at which the good or service was allegedly provided. The computer compares the two locations for the particular time and generates a fraud score based on a likelihood that the recipient was physically capable of being at the provider location at the particular time, and based on the score, may notify the recipient and decline the payment request.

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

The Walnut Healthcare Global System is a creative tool to achieve maximum productivity through increasing health literacy involving multi-talented groups of people and technology working together to achieve the best results to reduce healthcare costs. Together with discovering the drivers of spending and spending growth in US health care. How do the organization’s strategy and decision-making processes impact total spending and value, and how to control them? Lifelong educational offerings that develop management competencies and improving professional skills are vital to succeed through this tool.

The Self Power can be learned, it is that kind of power that helps you see things through your inner eye. For “The Self Power” to work, organizations need to create an environment that encourages creativity and innovation. After that, they should focus on bringing together multi-talented groups of individuals who collaborate to exchange ideas and knowledge to shape the direction of the future.

The Walnut Healthcare Global System is a creative tool to achieve maximum productivity through increasing health literacy involving multi-talented groups of people and technology working together to achieve the best results to reduce healthcare costs. Together with discovering the drivers of spending and spending growth in US health care. How do the organization’s strategy and decision-making processes impact total spending and value, and how to control them? Lifelong educational offerings that develop management competencies and improving professional skills are vital to succeed through this tool.

The Self Power can be learned, it is that kind of power that helps you see things through your inner eye. For “The Self Power” to work, organizations need to create an environment that encourages creativity and innovation. After that, they should focus on bringing together multi-talented groups of individuals who collaborate to exchange ideas and knowledge to shape the direction of the future.

A method and system for annotation and classification of biomedical text having bacterial associations have been provided. The method is microbiome specific method for extraction of information from biomedical text which provides an improvement in accuracy of the reported bacterial associations. The present disclosure uses a unique set of domain features to accurately identify bacterial associations from the biomedical text. The disclosure further provides a method to use the set of domain features to improve a microbiome crowd sourcing setup and create a refined microbial association network. The refined bacterial association network can also be made corresponding to a disease or healthy state, which can be used for an improved understanding of the bacterial community structure and design therapeutic interventions. This refined bacterial association networks for a disease can then be used for clinical, therapeutic and diagnostic applications for treatment of the disease.

Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.

Systems and methods for data warehouse management for medical information is provided. The system receives a set of medical record documents. These documents include evidence for one or more findings which are identified using natural language processing of evidence. The data set is probabilistically transformed into a structured data set (often as a table). This structured data set includes embedded links that reference the source document where the evidence is located. If the finding is supported by multiple articles of evidence, the link will direct the user to the source document with the highest confidence ranking. Evidence in the source document is highlighted and may include associated annotations. Evidence, findings and annotations may be updated by the user.