Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a computer-implemented method for generating a predictive output with respect to a primary audio data embedding data object associated with a primary audio data object, is provided. The method includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.

Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing a call drop likelihood prediction for an interactive call data object.

Abstract: There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a computer-implemented method for generating a predictive output with respect to a primary audio data embedding data object associated with a primary audio data object, is provided. The method includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.

Abstract: A system and method for medical literature monitoring of adverse drug relations, enabled by screening literature references by applying one or more machine learning models trained using a data labelling protocol and a plurality of data rules prescribed by a plurality of subject matter experts. The data labelling protocol comprises a set of inferences derived from screening and labelling a plurality of medical literature with suspected references to adverse drug reactions by subject matter experts. Suspected references to adverse drug reactions includes direct references to adverse drug reactions and indirect references to adverse drug reactions. The plurality of data rules is derived from observations of subject matter experts during data labelling. The predictions outputted by each of the machine learning models are validated with the data rules, and a final list of literature with suspected references to adverse drug reactions is generated.

Abstract: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing a call drop likelihood prediction for an interactive call data object.

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: Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

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: 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: 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: 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: 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.