Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for initiating presentation of an interactive user interface. The techniques may include receiving one or more candidate prediction-based actions and generating a plurality of causal risk-based impact scores with respect to a candidate prediction-based action. The techniques include generating a plurality of causal quality-based impact scores and an action sequence for a plurality of evaluation entities and generating a causal net impact score based on (i) an aggregation of the plurality of causal risk-based impact scores and the plurality of causal quality-based impact scores and (ii) a sequence impact metric corresponding to the action sequence. The techniques include generating a sequence ranking for the action sequence and initiating a presentation of an interactive user interface reflective of the action sequence and the sequence ranking.

Abstract: Various embodiments of the present disclosure provide feature engineering techniques for improving machine learning model development, performance, and maintenance. The feature engineering techniques include generating a model description vector from a textual model description for a target machine learning model and using the model description vector to generate one or more description-based similarity vectors that each include one or more of a plurality of similarity scores for a plurality of machine learning features. The feature engineering techniques include generating a label-based similarity vector based on a comparison between training data for the target machine learning model and a plurality of feature values of the plurality of machine learning features. The feature engineering techniques include providing a predictive feature set for the target machine learning model based on the one or more similarity vectors and the one or more label-based similarity vectors.

Abstract: This disclosure describes techniques for providing diagnosis and treatment recommendations using quantum computing. For example, a quantum computing device encodes diagnosis-relevant information of a patient as one or more patient diagnosis qubits. The quantum computing device implements a first quantum search algorithm using the patient diagnosis qubits to determine a diagnosis likelihood for each condition of a plurality of conditions. The quantum computing device further encodes the diagnosis data and treatment-relevant information of the patient as one or more patient treatment qubits. The quantum computing device implements a second quantum search algorithm using the treatment-relevant information of the patient to determine one or more treatment recommendations for the patient.

Abstract: Various embodiments of the present disclosure provide a optimized resource allocation using multi-level regression modeling that improves the functionality of a computer in various aspects. The techniques comprise synthesizing (i) a prediction stratification data structure that assigns a probability classification of a plurality of defined probability classifications for a requested class to an entity identifier of a recorded entity cohort with a (ii) a plurality of entity feature vectors that comprise an entity feature vector for the entity identifier of the recorded entity cohort to generate a multi-level regression model for the requested class.

Abstract: This disclosure describes techniques for providing diagnosis and treatment recommendations using quantum computing. For example, a quantum computing device encodes diagnosis-relevant information of a patient as one or more patient diagnosis qubits. The quantum computing device implements a first quantum search algorithm using the patient diagnosis qubits to determine a diagnosis likelihood for each condition of a plurality of conditions. The quantum computing device further encodes the diagnosis data and treatment-relevant information of the patient as one or more patient treatment qubits. The quantum computing device implements a second quantum search algorithm using the treatment-relevant information of the patient to determine one or more treatment recommendations for the patient.

Abstract: Systems and methods are disclosed for enhancing data. One or more processors may receive a data object associated with a user that includes a first parameter initially set to a first value. One or more processors may determine, based on a comparison of parameters of the data object with corresponding parameters of data objects associated with other users, that one or more of (1) a second value should override the first value or (2) a second parameter should be added into the data object. One or more processors may generate an augmented data object by modifying the data object to include the second value or the second parameter based on the determining. One or more processors may store or delete information about the user in memory based on the augmented data object.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for initiating presentation of an interactive user interface. The techniques may include receiving one or more candidate prediction-based actions and generating a plurality of causal risk-based impact scores with respect to a candidate prediction-based action. The techniques include generating a plurality of causal quality-based impact scores and an action sequence for a plurality of evaluation entities and generating a causal net impact score based on (i) an aggregation of the plurality of causal risk-based impact scores and the plurality of causal quality-based impact scores and (ii) a sequence impact metric corresponding to the action sequence. The techniques include generating a sequence ranking for the action sequence and initiating a presentation of an interactive user interface reflective of the action sequence and the sequence ranking.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, causal risk-based scores. The techniques may include generating a predictive risk-based opportunity score for an evaluation entity based on (i) a plurality of engagement scores and (ii) a plurality of predictive risk scores respectively corresponding to a plurality of predictive entities within an entity cohort associated with the evaluation entity. Using action-specific causal inference models, a predictive impact score of a prediction-based action on the evaluation entity is generated and used to generate a causal gap closure score for the evaluation entity based on a gap closure rate associated with the evaluation entity. The techniques include generating a causal risk-based impact score for the prediction-based action and the evaluation entity based on the predictive risk-based opportunity score, the predictive impact score, and a predictive improvement measure.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, categorical improvement predictions. The techniques may include generating a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group. The techniques include using an action-specific causal inference model to generate a metric-specific predictive impact measure. The techniques include generating a metric-level categorical improvement prediction and a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics.

Abstract: Various embodiments of the present disclosure provide feature engineering techniques for improving machine learning model development, performance, and maintenance. The feature engineering techniques include generating a model description vector from a textual model description for a target machine learning model and using the model description vector to generate one or more description-based similarity vectors that each include one or more of a plurality of similarity scores for a plurality of machine learning features. The feature engineering techniques include generating a label-based similarity vector based on a comparison between training data for the target machine learning model and a plurality of feature values of the plurality of machine learning features. The feature engineering techniques include providing a predictive feature set for the target machine learning model based on the one or more similarity vectors and the one or more label-based similarity vectors.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for performing predictive recommendation. In one example embodiment, a method is provided. The method includes generating guideline data objects for a plurality of service need conditions. The method includes generating a compliance profile data object for each of a plurality of provider entities. The compliance profile data object for a provider entity includes compliance scores with respect to the plurality of service need conditions, a compliance score determined using procedural record data objects associated with each provider entity and the guideline data objects. The method further includes selecting a subset of the plurality of provider entities according to the compliance profile data object for each provider entity. The method further includes performing at least one automated recommendation-based action based at least in part on the selected subset.

Abstract: This disclosure describes techniques for providing diagnosis and treatment recommendations using quantum computing. For example, a quantum computing device encodes diagnosis-relevant information of a patient as one or more patient diagnosis qubits. The quantum computing device implements a first quantum search algorithm using the patient diagnosis qubits to determine a diagnosis likelihood for each condition of a plurality of conditions. The quantum computing device further encodes the diagnosis data and treatment-relevant information of the patient as one or more patient treatment qubits. The quantum computing device implements a second quantum search algorithm using the treatment-relevant information of the patient to determine one or more treatment recommendations for the patient.

Abstract: Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for performing predictive recommendation. In one example embodiment, a method is provided. The method includes generating guideline data objects for a plurality of service need conditions. The method includes generating a compliance profile data object for each of a plurality of provider entities. The compliance profile data object for a provider entity includes compliance scores with respect to the plurality of service need conditions, a compliance score determined using procedural record data objects associated with each provider entity and the guideline data objects. The method further includes selecting a subset of the plurality of provider entities according to the compliance profile data object for each provider entity. The method further includes performing at least one automated recommendation-based action based at least in part on the selected subset.