Abstract: Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient financial experience and satisfaction in the revenue cycle (Barkholz, 2017). However, constructing advanced predictive analytics models has been considered challenging in the last twenty years. That said, we propose a (low-level) context-dependent compact representation of patients' historical claim records by effectively learning complicated dependencies in the (high-level) claim inputs. Built on this new latent representation, we demonstrate that a deep learning-based framework, Deep Claim, can accurately predict various responses from multiple payers using 2,905,026 de-identified claims data from two US health systems.

Abstract: Embodiments provide for improvements in generating focused data from an unstructured data set. The focused data generated from the unstructured data set may provide data insight(s) into analysis of the unstructured data set, and/or provide for improved capabilities for a user to efficiently navigate through relevant data portions of such data utilizing a user interface, even when such relevant data portions are not immediately distinguishable without further processing of the unstructured data set.

Abstract: Embodiments provide for improvements in generating focused data from an unstructured data set. The focused data generated from the unstructured data set may provide data insight(s) into analysis of the unstructured data set, and/or provide for improved capabilities for a user to efficiently navigate through relevant data portions of such data utilizing a user interface, even when such relevant data portions are not immediately distinguishable without further processing of the unstructured data set.

Abstract: Various embodiments of the present disclosure provide a segment-wise prediction machine learning framework. In one example, an embodiment provides for generating, using a segment-wise prediction machine learning framework, and based at least in part on a document segment for an input segment and a respective predictive code for the input segment, a segment-wise prediction score for the input segment. The segment-wise prediction machine learning framework may comprise a text embedding machine learning model and may be configured to generate a segment-wise prediction score for the input segment based at least in part on a document embedding for the input segment and a code embedding for the respective predictive code for the input segment. Additionally, the text embedding machine learning model may be trained as part of a code prediction machine learning model that is configured to generate, for a particular input document data object, a selected code subset.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive mapping operations with respect to a ground-truth database table. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive mapping operations utilizing a hierarchical string matching machine learning framework using at least one or more of an exact match model, a probabilistic match model, a disjoint match model, and an embedding-based match model.

Abstract: An apparatus, method, and computer program product for the improved development of training data sets for use in connection with machine learning models capable of operating on natural language data records and other unstructured data in a network environment. Some example implementations provide for the generation and presentation of record images in a user interface that allows captures user actions reflecting higher-order data analysis and discernment for incorporation into training protocols used for machine learning models.

Abstract: An apparatus, method, and computer program product for the improved development of training data sets for use in connection with machine learning models capable of operating on natural language data records and other unstructured data in a network environment. Some example implementations provide for the generation and presentation of record images in a user interface that allows captures user actions reflecting higher-order data analysis and discernment for incorporation into training protocols used for machine learning models.