Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.

Abstract: To detect anomalous activity within a multi-provider environment of transactional data, a particular target entity of a plurality of entities, such as a provider, is identified; multiple relationships associated with the multi-provider environment are determined, wherein each relationship is associated with a relationship score, and wherein the relationship score is determined based on a relational proximity criterion satisfied by the relationship; one or more risk scores are generated; a network risk score is generated for the target entity, multiple levels of related entities are identified, and anomalous activity detection is performed based on the network risk score. Anomalous activity and entity relationships are presented in a graph interface.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: There is a need for more accurate and more efficient database management operations. This need can be addressed by, for example, techniques for efficient matching of data fields in response to database queries. In one example, a method includes: for each input data field of a plurality of input data fields, generating an per-field encoded representation of the input data field based on each per-character increment score for an occurred character in the input data field; performing the automated data field matching based on each per-field encoded representation for an input data field to generate one or more data field matching outputs; and causing display of the one or more data field matching determinations using a data field matching output interface.

Abstract: Without timely reminders, certain individuals may be highly unlikely to complete various tasks scheduled for completion. For example, medicinal therapy regimen patients often have difficulty in remembering to take their prescribed medications in accordance with care provider specified instructions. By providing a reminder prompt generation system that utilizes machine-learning and/or artificial intelligence to determine appropriate reminder times and modalities for providing automated-prompting notifications to users, the users' adherence to scheduled task completions can be significantly increased.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for optimizing interaction flows. For example, upon receiving an inbound interaction, the interaction is routed to either a baseline interaction flow or one of a plurality of experimental interaction flows. Using one or more target metrics, the experimental interaction flows can be monitored and further configured to optimize the interaction flows and parameters.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.

Abstract: There is a need for solutions that increase efficiency and reliability of medical record processing systems. This need can be addressed, for example, by receiving a free-form primary provider input from a primary provider profile; detecting one or more information deficiencies in the primary provider input, wherein each information deficiency indicates a respective query; for each identified information deficiency of the one or more information deficiencies, identifying a supportive profile associated with the respective information deficiency, causing the supportive profile computing entity associated with the respective supportive provider profile to present an audiovisual interface that includes an indication of the respective query associated with the information deficiency, and receiving a supportive provider input from each supportive provider profile; and generating a collaborative record for the primary provider input based on the primary provider input and each received supportive provider input.

Abstract: Various embodiments of the present disclosure are directed to model feature classification, analysis, and updating for analyzing one or more risk determination model(s). Embodiments include an improved apparatus configured to generate an actionable feature data object subset of a risk model feature set for a risk determination model, for example using an actionable determination model. The apparatus may provide the actionable feature data object subset for rendering to an interface of a client device. The apparatus may additionally or alternatively be configured to utilize user feedback, provided either directly or identified by the apparatus based on user interactions, to update the actionable determination model. The apparatus may additionally or alternatively be configured to maintain and utilize linked claim data object(s) for use in rendering a linked claim scores analysis interface that provides additional insight regarding the risk level of a particular entity.

Abstract: A system and method for tokenizing sentences in transcriptions of electronic communication audio files includes one or more electronic data storage systems coupled in electronic communication with an audio receiver. A collection of audio files is stored in the electronic data storage system(s). The audio receiver is configured to receive one or more audio files from the electronic data storage systems. The system also includes a speech-to-text processing engine configured to transcribe the one or more audio files received by the audio receiver to produce input transcriptions. In addition, the system includes a call tokenization engine that stores a list of candidate expressions and expression pairs that are indicative of turn taking and change of context in phone conversations.

Abstract: Machine-learning concepts for detecting and visualizing healthcare fraud, waste, and abuse risk using a data driven decision and investigations support system. The concepts comprise an analytic core that processes a large amount of data, generates an overall risk score, and ranks healthcare providers and/or members. The overall risk score is an integrated score encompassing multiple categories of risk. The multiple categories of risk factors include multiple definitions of what defines risk, allowing for a synergistic effect between the risk analytics, where the overall effect of the combination is greater than the sum of the effects of any one definition of risky behavior by a provider or member. Utilizing this approach a unique risk profile of healthcare providers and members is generated and visualized to the user. Various embodiments further encompass a user interface that comprises linked panels that display targeted information regarding providers and members.

Abstract: A system and method for tokenizing sentences in transcriptions of electronic communication audio files includes one or more electronic data storage systems coupled in electronic communication with an audio receiver. A collection of audio files is stored in the electronic data storage system(s). The audio receiver is configured to receive one or more audio files from the electronic data storage systems. The system also includes a speech-to-text processing engine configured to transcribe the one or more audio files received by the audio receiver to produce input transcriptions. In addition, the system includes a call tokenization engine that stores a list of candidate expressions and expression pairs that are indicative of turn taking and change of context in phone conversations.