Abstract: The present disclosure provides methods, apparatus, systems, computing devices, and/or the like for performing risk prediction by receiving outer cohort definition data and inner cohort definition data, the outer cohort definition data representative of a target data domain with respect to a dataset, and the inner cohort definition data representative of a prediction feature with respect to the target data domain, determining one or more inner cohort features based at least in part on a knowledge graph data object using the inner cohort definition data, the knowledge graph data object including co-occurrence information of features from the dataset, and generating, using a predictive machine learning model, for each of one or more outer cohort entities associated with features in an outer cohort data subset, a risk score representative of a propensity of the outer cohort entity being an inner cohort entity associated with features in an inner cohort data subset.

Abstract: There is a need to perform predictive inference to predict likely adverse events of a drug regimen consisting of multiple drugs. In one example, a method includes determining, based at least in part on a graph-based predictive database, one or more predictive categories for each patient node of a plurality of patient nodes; determining, based at least in part on each one or more predictive categories for a patient node and each of one or more patient attribute nodes for a patient node, a related patient cohort for the primary patient node, wherein the related patient cohort comprises the primary patient node and one or more related patient nodes; determining, based at least in part on one or more intake relationships for each patient node in the related patient cohort, a first related drug profile for the primary patient node; and generating a first prediction interface based at least in part on the first related drug profile.

Abstract: Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

Abstract: Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.

Abstract: Method and apparatus for performing feature engineering using negative inferences are provided. One example method generally includes identifying a plurality of concepts and analyzing a corpus of documents to determine a first co-occurrence rate for a first concept and a second concept in the plurality of concepts. The method further includes analyzing the corpus of documents to determine a second co-occurrence rate for the second concept and at least a third concept of a set of concepts related to the first concept and determining an inverse relationship between the second concept and the third concept. The method further includes generating test data for training a machine learning model including a negative inference between the second concept and the third concept and training the machine learning model using the test data.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing risk score generation predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform risk score generation predictive data analysis by utilizing at least one of event-based confidence scores and delay-based confidence scores.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing risk score generation predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that risk score generation predictive data analysis by utilizing at least one of inferred hybrid risk score generation machine learning models and hybrid graph-based machine learning models.

Abstract: A computing system receives a plurality of medical perspectives for each medical treatment option of a plurality of medical treatment options. A machine learning model assigns a weight to each medical perspective for each medical treatment option by determining how often a care provider has agreed with each perspective within the plurality of medical perspectives. The computing system receives a medical treatment option ranking for each of the medical perspectives. The computing system generates a score for each medical perspective as applied to each medical treatment option based on a combination of the weight and the ranking for each medical perspective. The computing system aggregates the scores across the plurality of medical perspectives into an overall score for each medical treatment option.

Abstract: Systems and methods are configured to perform machine-learning-based predictive behavioral response. In various embodiments, one or more behavioral monitoring data objects are identified and processed using a behavioral pattern prediction machine learning model to generate a behavioral pattern prediction model. The behavioral pattern prediction model is processed using a risk generation machine learning model to generate a risk model, wherein: (i) the risk generation machine learning model is generated based at least in part by one or more risk factors, and (ii) the risk model comprises a per-risk factor score for each risk factor of the one or more risk factors. The risk model is processed using an adjustment generation machine learning model to generate an adjustment model and one or more prediction-based actions are performed based on the adjustment model.

Abstract: Embodiments provide for reduced user input requests by identifying predefined diagnosis paradigms; creating a synthetic diagnosis paradigm via a machine learning process based on prior selections of action plans recommended by the predefined diagnosis paradigms and values entered therefor, wherein the synthetic diagnosis paradigm identifies the action plans to treat the plurality of conditions based on a subset of the attribute inputs used by the predefined diagnosis paradigms; generating a graphical user interface (GUI) to prompt input for values for the subset of attribute inputs; in response to receiving the values for the subset of attribute inputs, identifying at least one condition according to the predefined and synthetic diagnosis paradigms; and displaying the action plans in the GUI in association with the synthetic and predefined diagnosis paradigms according to evaluations of the action plans based on the respective logical structures.

Abstract: Methods and systems for presenting a proactive audiovisual query using a virtual assistant software application. The methods correspond to retrieving user experience data associated with a user profile for the virtual assistant software application, wherein the user experience data define one or more user experience events for the user profile. The methods further include retrieving subject matter domain data associated with the one or more user experience events, wherein the subject matter domain data define one or more probabilistic event effects for the one or more user experience events and determining, based on the user experience data and the subject matter domain data, one or more optimal query items, wherein the one or more optimal query items are associated with at least one of the one or more probabilistic event effects. The methods further include generating the proactive audiovisual query in accordance with the one or more optimal query items.

Abstract: There is a need to perform predictive inference to predict likely adverse events of a drug regimen consisting of multiple drugs. In one example, a method includes determining, based at least in part on a graph-based predictive database, one or more predictive categories for each patient node of a plurality of patient nodes; determining, based at least in part on each one or more predictive categories for a patient node and each of one or more patient attribute nodes for a patient node, a related patient cohort for the primary patient node, wherein the related patient cohort comprises the primary patient node and one or more related patient nodes; determining, based at least in part on one or more intake relationships for each patient node in the related patient cohort, a first related drug profile for the primary patient node; and generating a first prediction interface based at least in part on the first related drug profile.

Abstract: Embodiments provide for the dynamic adjustment of graphical user interfaces (GUIs) in response to learned user preferences via generating a plurality of scores for a plurality of action plans based on logical structures defined by a plurality of diagnosis paradigms that identify conditions addressable by individual action; determining a plurality of weights associated with the plurality of diagnosis paradigms, wherein a given weight of the plurality of weights is based on a historic frequency of selection of individual action plans from a GUI associated with a particular diagnosis paradigm; determining a concordance measure for each action plan relative to each other action plan based on a machine learning clustering of the action plans using the plurality of weights and the plurality of scores; and generating the GUI to present the plurality of diagnosis paradigms and the plurality of action plans based on a respective concordance measure for each action plan.

Abstract: A computing system receives a plurality of medical perspectives for each medical treatment option of a plurality of medical treatment options. A machine learning model assigns a weight to each medical perspective for each medical treatment option by determining how often a care provider has agreed with each perspective within the plurality of medical perspectives. The computing system receives a medical treatment option ranking for each of the medical perspectives. The computing system generates a score for each medical perspective as applied to each medical treatment option based on a combination of the weight and the ranking for each medical perspective. The computing system aggregates the scores across the plurality of medical perspectives into an overall score for each medical treatment option.

Abstract: Method and apparatus for performing feature engineering using negative inferences are provided. One example method generally includes identifying a plurality of concepts and analyzing a corpus of documents to determine a first co-occurrence rate for a first concept and a second concept in the plurality of concepts. The method further includes analyzing the corpus of documents to determine a second co-occurrence rate for the second concept and at least a third concept of a set of concepts related to the first concept and determining an inverse relationship between the second concept and the third concept. The method further includes generating test data for training a machine learning model including a negative inference between the second concept and the third concept and training the machine learning model using the test data.

Abstract: Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

Abstract: An apparatus, system, and storage medium that, in an embodiment, receive a constraint command that specifies a parent table, a primary key in the parent table, a child table, and a foreign key in the child table, and enforce that all values for the primary key in the parent table are present in the foreign key in the child table. In an embodiment, the enforcing may include receiving an insert command that specifies a target key, a target value for the target key, and a target table; determining whether the target table matches the parent table and whether, within a transaction that includes the insert command, the child table includes at least one row with a foreign key value that equals the target value of the primary key; inserting the target value in the target table if the determining is true; and returning an error otherwise.

Abstract: A method and apparatus for performing a cascading copy operation in a database with referential integrity constraints is disclosed. A user may supply (or be prompted to provide) a new key value, a first table, and a primary key value to use in the cascading copy operation. In response, a database engine may be configured to create a new record in the first table, with a primary key equal to the new key value supplied to the cascading copy operation and otherwise duplicate data values from the row identified by the primary key value. The database engine may also duplicate records in the database in tables other than the first table. Specifically, in one embodiment, records in any table that use the primary key value supplied to the cascading copy operation as a foreign key may be duplicated as a new record having the new key value as the foreign key.

Abstract: An apparatus, system, and storage medium that, in an embodiment, receive a constraint command that specifies a parent table, a primary key in the parent table, a child table, and a foreign key in the child table, and enforce that all values for the primary key in the parent table are present in the foreign key in the child table. In an embodiment, the enforcing may include receiving an insert command that specifies a target key, a target value for the target key, and a target table; determining whether the target table matches the parent table and whether, within a transaction that includes the insert command, the child table includes at least one row with a foreign key value that equals the target value of the primary key; inserting the target value in the target table if the determining is true; and returning an error otherwise.

Abstract: A method, apparatus, system, and signal-bearing medium that, in an embodiment, receive a constraint command that specifies a parent table, a primary key in the parent table, a child table, and a foreign key in the child table, and enforce that all values for the primary key in the parent table are present in the foreign key in the child table. In an embodiment, the enforcing may include receiving an insert command, wherein the insert command specifies a target key, a target value for the target key, and a target table; determining whether the target table matches the parent table and whether, within a transaction that includes the insert command, the child table includes at least one row with a foreign key value that equals the target value of the primary key; inserting the target value in the target table if the determining is true; and returning an error otherwise.