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 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 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 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 marked up document. The method may include: detecting a plurality of fields representing demographic information in a marked up document; extracting a set of features based the detected demographic information; based at least in part on the set of features, determining whether the plurality of fields represent demographic information of a single provider using a machine learning model trained according to other marked up documents; and when the plurality of fields are determined to represent demographic information of a single provider, associating the plurality of fields to represent demographic information of the single provider.