Abstract: Disclosed herein are system, method, and computer program product aspects for managing and recommending changes of demographic information. An example implementation may operate by receiving a data file containing a plurality of changes associated with one or more fields of one or more entries of demographic information. The implementation may then aggregate the data file based on identifying the plurality of changes in the one or more fields of demographic information to generate an aggregated data file. The implementation may then generate a user interface to request a user to provide a user preference. The implementation may then compare a level of aggressiveness provided by the user to respective scores associated with one or more changes within the one or more entries of the aggregated data file to identify a subset of the one or more changes to apply. The implementation may then apply the subset of the one or more changes to the data file to generate an updated data file.

Abstract: The present disclosure is directed to systems and methods for identifying demographic information in a data file. The method may include: receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having inconsistent or mislabeled nomenclatures for one or more fields of the plurality of fields or spurious demographic information; analyzing the data file using a machine learning model trained according to other data files to distinguish between each of the plurality of fields of demographic information, the machine learning model being based on a plurality of machine learning algorithms to identify different types demographic information; generating a score indicating a probability that each of the plurality of fields of demographic information was identified correctly; and generating a revised data file labeling each of the plurality of fields of demographic information based on the identified type.

Abstract: The present disclosure is directed to systems and methods for identifying demographic information in a data file. The method uses evidence for a label within the column itself. In addition, the method uses likelihoods that a first label may exist at a particular location in a data file with respect to a second label. Finally, the method uses likelihoods that a first label exists in the data file at a first frequency given that a second label exists in a data file in a second frequency. All based on these likelihoods, an overall likelihood of that the label configuration is correct is determined. Using that likelihood score, a nonlinear optimization algorithm is applied to identify a best fit between a group of labels and a group of columns in a data file.

Abstract: The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file and converting that data into autonomous data of an export entity file. In an embodiment, the received tabular data is sorted based on a lowest number of unique entities of a certain type in the revised data file, common data describing the lowest number of unique entities is extracted, labeled, and stored as an autonomous export entity file. In an embodiment a plurality of autonomous export entities are linked to create a master export entity comprising the plurality of autonomous export entities.

Abstract: Disclosed herein are system, method, and computer program product embodiments for linking data records in memory. The system, method, and computer program product includes accessing a first record stored in memory, the first record holding information describing a first person and accessing at least one additional record stored in memory, the additional records holding information describing additional persons. The method continues by parsing the information of the first record and additional record and assigning the parsed information to predefined categories within the respective records. After assigning the information into categories, a similarity score between categorical information in the first record and categorical information of additional records is determined. A category of an additional record is then modified based on the similarity score, so the additional record is associated with the first person.

Abstract: The present disclosure is directed to systems and methods for extracting unstructured data from a data source in a structure manner. Embodiments provide ways to retrieve unstructured data along from data sources not optimized for automated retrieval. For example, embodiments may generate a branched tree for each data source that maps out paths to individual sites of, for example, a healthcare provider listing the unstructured data. Using this branched tree, tasks can be generated to navigate along a path with the data source to each site and extract the unstructured data from the data source. In this way, embodiments provide the ability to navigate through a site from a base site to a site that has the relevant data.

Abstract: The present disclosure is directed to systems and methods for identifying demographic information in a data file. The method may include: receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having inconsistent or mislabeled nomenclatures for one or more fields of the plurality of fields or spurious demographic information; analyzing the data file using a machine learning model trained according to other data files to distinguish between each of the plurality of fields of demographic information, the machine learning model being based on a plurality of machine learning algorithms to identify different types demographic information; generating a score indicating a probability that each of the plurality of fields of demographic information was identified correctly; and generating a revised data file labeling each of the plurality of fields of demographic information based on the identified type.

Abstract: The present disclosure is directed to systems and methods for extracting unstructured data from a data source in a structure manner. Embodiments provide ways to retrieve unstructured data along from data sources not optimized for automated retrieval. For example, embodiments may generate a branched tree for each data source that maps out paths to individual sites of, for example, a healthcare provider listing the unstructured data. Using this branched tree, tasks can be generated to navigate along a path with the data source to each site and extract the unstructured data from the data source. In this way, embodiments provide the ability to navigate through a site from a base site to a site that has the relevant data.

Abstract: The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file even in the face of known errors that would otherwise prevent the method from operating. When a fault condition is detected, a fault handler may attempt to fix the faulty data, remove the faulty data from the input data file being processed, or provide the faulty data at a user interface so a human user can intervene. The method and storage devices may continue processing the data regardless of whether human input has been received because the system can bypass or remove the data, thereby keeping the method fault tolerant.

Abstract: The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file and converting that data into autonomous data of an export entity file. In an embodiment, the received tabular data is sorted based on a lowest number of unique entities of a certain type in the revised data file, common data describing the lowest number of unique entities is extracted, labeled, and stored as an autonomous export entity file. In an embodiment a plurality of autonomous export entities are linked to create a master export entity comprising the plurality of autonomous export entities.

Abstract: Disclosed herein are system, method, and computer program product embodiments for linking data records in memory. The system, method, and computer program product includes accessing a first record stored in memory, the first record holding information describing a first person and accessing at least one additional record stored in memory, the additional records holding information describing additional persons. The method continues by parsing the information of the first record and additional record and assigning the parsed information to predefined categories within the respective records. After assigning the information into categories, a similarity score between categorical information in the first record and categorical information of additional records is determined. A category of an additional record is then modified based on the similarity score, so the additional record is associated with the first person.

Abstract: The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file even in the face of known errors that would otherwise prevent the method from operating. When a fault condition is detected, a fault handler may attempt to fix the faulty data, remove the faulty data from the input data file being processed, or provide the faulty data at a user interface so a human user can intervene. The method and storage devices may continue processing the data regardless of whether human input has been received because the system can bypass or remove the data, thereby keeping the method fault tolerant.

Abstract: To train models, training data is needed. As personal data changes over time, the training data can get stale, obviating its usefulness in training the model. Embodiments deal with this by developing a database with a running log specifying how each person's data changes at the time. When data is ingested, it may not be normalized. To deal with this, embodiments clean the data to ensure the ingested data fields are normalized. Finally, the various tasks needed to train the model and solve for accuracy of personal data can quickly become cumbersome to a computing device. They can conflict with one another and compete inefficiently for computing resources, such as processor power and memory capacity. To deal with these issues, a scheduler is employed to queue the various tasks involved.

Abstract: The present disclosure is directed to systems and methods for identifying demographic information in a data file. The method uses evidence for a label within the column itself. In addition, the method uses likelihoods that a first label may exist at a particular location in a data file with respect to a second label. Finally, the method uses likelihoods that a first label exists in the data file at a first frequency given that a second label exists in a data file in a second frequency. All based on these likelihoods, an overall likelihood of that the label configuration is correct is determined. Using that likelihood score, a nonlinear optimization algorithm is applied to identify a best fit between a group of labels and a group of columns in a data file.

Abstract: The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file and converting that data into autonomous data of an export entity file. In an embodiment, the received tabular data is sorted based on a lowest number of unique entities of a certain type in the revised data file, common data describing the lowest number of unique entities is extracted, labeled, and stored as an autonomous export entity file. In an embodiment a plurality of autonomous export entities are linked to create a master export entity comprising the plurality of autonomous export entities.

Abstract: The present disclosure is directed to methods and non-transitory program storage devices for identifying demographic information in an input data file even in the face of known errors that would otherwise prevent the method from operating. When a fault condition is detected, a fault handler may attempt to fix the faulty data, remove the faulty data from the input data file being processed, or provide the faulty data at a user interface so a human user can intervene. The method and storage devices may continue processing the data regardless of whether human input has been received because the system can bypass or remove the data, thereby keeping the method fault tolerant.

Abstract: Disclosed herein are system, method, and computer program product embodiments for linking data records in memory. The system, method, and computer program product includes accessing a first record stored in memory, the first record holding information describing a first person and accessing at least one additional record stored in memory, the additional records holding information describing additional persons. The method continues by parsing the information of the first record and additional record and assigning the parsed information to predefined categories within the respective records. After assigning the information into categories, a similarity score between categorical information in the first record and categorical information of additional records is determined. A category of an additional record is then modified based on the similarity score, so the additional record is associated with the first person.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining a first processing demand required to clean a data set and identifying one or more processors to clean the data based on the determined demand. The system, method, and computer program product embodiment further monitors the data cleaning process to determine a second demand and decides whether to continue operating one or more of the processors, based on the second demand.

Abstract: The present disclosure is directed to systems and methods for identifying demographic information in a data file. The method may include: receiving the data file containing a plurality of fields of demographic information from a third-party, the data file having inconsistent or mislabeled nomenclatures for one or more fields of the plurality of fields or spurious demographic information; analyzing the data file using a machine learning model trained according to other data files to distinguish between each of the plurality of fields of demographic information, the machine learning model being based on a plurality of machine learning algorithms to identify different types demographic information; generating a score indicating a probability that each of the plurality of fields of demographic information was identified correctly; and generating a revised data file labeling each of the plurality of fields of demographic information based on the identified type.

Abstract: The present disclosure is directed to systems and methods for extracting unstructured data from a data source in a structure manner. Embodiments provide ways to retrieve unstructured data along from data sources not optimized for automated retrieval. For example, embodiments may generate a branched tree for each data source that maps out paths to individual sites of, for example, a healthcare provider listing the unstructured data. Using this branched tree, tasks can be generated to navigate along a path with the data source to each site and extract the unstructured data from the data source. In this way, embodiments provide the ability to navigate through a site from a base site to a site that has the relevant data.