Abstract: Various embodiments of the present disclosure provide a hybrid machine learning process that improves the functionality of a computer in various aspects. The techniques comprise generating, using a supervised machine learning model of a connected model framework, a predictive feature for an entity of a set of entities based on a set of entity attributes corresponding to the entity. The techniques comprise generating, using a clustering model of the connected model framework, a refined entity cluster by (i) generating an initial cluster for the entity that comprises a first subset of the set of entities, (ii) generating, based on the predictive feature, a coefficient of variation for the initial cluster, and (iii) generating the refined cluster from the initial cluster based on the coefficient of variation. The techniques comprise generating a modified predictive feature for the entity based on the refined cluster.

Abstract: Various embodiments of the present disclosure provide a genetic algorithm-based generative learning system for synthetic text generation that comprises generating using an encoder of a generative machine learning model, a sample text embedding of a corpus sample; generating using a decoder of the generative machine learning model, a synthetic sample based on the sample text embedding of the corpus sample; generating using the encoder, a synthetic text embedding of the synthetic sample; generating using a cost function, a similarity measure for the synthetic sample based on a first comparison between the synthetic text embedding and the sample text embedding; generating using the cost function, a variation measure for the synthetic sample based on a second comparison between the synthetic text embedding and the sample text embedding; and providing a model performance score for the generative machine learning model based on a comparison between the similarity measure and the variation measure.

Abstract: A computer-implemented method for identifying a problem list section from an electronic document includes receiving, by one or more processors, the electronic document, generating, by the one or more processors and based on applying an optical character recognition algorithm to the electronic document, unstructured text, and identifying, by the one or more processors, one or more problem list words in the unstructured text, the one or more problem list words belonging in a dataset for identifying a presence of a problem list section. The method also includes associating, by the one or more processors, a portion of the unstructured text that corresponds to the one or more problem list words in the unstructured text with the problem list section and outputting, by the one or more processors, at least a portion of the problem list section.

Abstract: Systems and methods for targeted medical document review are disclosed. A list of medical documents is received. Each medical document is associated with a user enrolled in a plan subject to a medical document review process. A dataset for each medical document, including clinical data, membership data, and provider data associated with the user, is received. A first model is used to determine whether each medical document includes an undocumented condition based on the dataset. The list is reduced to a subset of medical documents determined to include an undocumented condition. A second model is used to determine a risk score associated with each medical document of the subset based on the dataset. The subset of medical documents are ordered in the reduced list based on the risk scores. The ordered, reduced list is provided as input to the medical document review process.

Abstract: A computer-implemented method for identifying a problem list section from an electronic document includes receiving, by one or more processors, the electronic document, generating, by the one or more processors and based on applying an optical character recognition algorithm to the electronic document, unstructured text, and identifying, by the one or more processors, one or more problem list words in the unstructured text, the one or more problem list words belonging in a dataset for identifying a presence of a problem list section. The method also includes associating, by the one or more processors, a portion of the unstructured text that corresponds to the one or more problem list words in the unstructured text with the problem list section and outputting, by the one or more processors, at least a portion of the problem list section.

Abstract: Systems and methods for targeted medical document review are disclosed. A list of medical documents is received. Each medical document is associated with a user enrolled in a plan subject to a medical document review process. A dataset for each medical document, including clinical data, membership data, and provider data associated with the user, is received. A first model is used to determine whether each medical document includes an undocumented condition based on the dataset. The list is reduced to a subset of medical documents determined to include an undocumented condition. A second model is used to determine a risk score associated with each medical document of the subset based on the dataset. The subset of medical documents are ordered in the reduced list based on the risk scores. The ordered, reduced list is provided as input to the medical document review process.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.