Optum Technology, Inc.

There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.

Solutions for more efficient and effective predictive code recommendation are disclosed. In one example, a method includes identifying a graph-based code recommendation machine learning model, wherein each inferred edge weight value of the graph-based code recommendation machine learning model is updated based at least in part on each compressed forward-adjusted temporal distance measure for an observed co-occurrence of any observed co-occurrences of a predictive code pair for the inferred edge weight value within one or more temporally-proximate occurrence subsets determined based at least in part on a plurality of training predictive code occurrences; processing the input predictive code using the graph-based code recommendation machine learning model to generate one or more related codes of the plurality of predictive codes for the input predictive code; and performing one or more prediction-based actions based at least in part on the one or more related codes.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of techniques using time bound code transition likelihood data objects, techniques using cross-code relationship values, techniques using augmented entity-code occurrence data objects, techniques using per-pathway text representations of inferred occurrence pathways of a one or more individual historic code occurrences, techniques using polygenic risk score (PRS) measures, and/or the like.

There is a need for improving data security in enrollment management systems. This need can be addressed by, for example, solutions for determining an enrollment recommendation for a primary member profile based on preconfigured enrollment modeling data. In one example, a method includes retrieving enrollment modeling data for a group of member profiles, determining a plurality of related member profiles for the primary member profile from the group of member profiles, determining a cross-member enrollment prediction for the primary member profile by comparing enrollment modeling data of the primary member profile and enrollment modeling data of each related member profile, determining a member-specific enrollment recommendation by comparing enrollment modeling data of the primary member profile and enrollment coverage criteria for each enrollment plan, and determining the enrollment recommendation based on the cross-member enrollment prediction and the member-specific enrollment prediction.

A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

There is a need for more effective and efficient anomalous text detection. This need can be addressed by, for example, solutions for anomalous text detection that include the steps of performing a group of exploration-exploitation keyword extraction iterations based at least in part on one or more training corpus data entries until a per-iteration keyword list for an ultimate exploration-exploitation keyword extraction iteration satisfies a keyword list threshold condition; and subsequent to performing the exploration-exploitation keyword extraction iterations: processing one or more input corpus data entries using the language-model-based binary classification model to generate one or more inferred anomaly probabilities, processing the one or more input corpus data entries using the keyword model to generate explanatory metadata for the one or more inferred anomaly probabilities, and performing one or more prediction-based actions based at least in part on the one or more inferred anomaly probabilities and the explanatory metadata.

There is a need for more accurate and more efficient 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 method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

There is a need for more effective and efficient detection of cross-data-column relationships. This need can be addressed by, for example, techniques for detecting cross-data-column data relationships that utilize at least one of feature-based similarity models and deep-learning-based similarity models. The cross-data-column data relationships may be displayed to an end-user using a cross-column relationship detection user interface.

There is a need for more accurate and more efficient 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 method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

There is a need for more effective and efficient text categorization. This need can be addressed by, for example, techniques for semantic text categorization. In one example, a method includes determining an input vector-based representation of an input document; processing the input vector-based representation using a trained supervised machine learning model to generate the categorization based at least in part on the input vector-based representation, wherein: (i) the trained supervised machine learning model has been trained using automatically-generated training data, and (ii) the automatically generated training data is generated by determining an inferred semantic label for each unlabeled training document of one or more unlabeled training documents; and performing one or more categorization-based actions based at least in part on the categorization, and (iii) the labels are described by one or more short documents/short texts.