Abstract: Improved computer modeling techniques, and uses thereof, are described herein. A set of unstructured textual data is received. Certain textual data is removed from the set of unstructured data to form an initial vocabulary set of textual data. One or more bigrams are added to the vocabulary set of textual data to form a final vocabulary set of textual data. The final vocabulary set of textual data is divided into a plurality of subsets of textual data based on type. A model is trained using each of the plurality of subsets of textual data to form a plurality of trained models, each corresponding to one of the types.

Abstract: A method includes receiving survey information associated with a patient, the survey information comprising quality of life survey answers for questions associated with a quality of life category; automatically processing the survey information using an Artificial Intelligence (AI) alert decision support system to perform operations comprising: generating an embedding for the survey information; and using a model corresponding to the quality of life category to predict a change in a quality of life score for the patient for the quality of life category based on the embedded survey information; comparing the change in the quality of life score for the patient with a threshold; and generating an alert based on the comparison of the change in the quality of life score for the patient with the threshold.

Abstract: Model metadata for each of a plurality of mathematical models is stored. Data describing two sets of models is received. Each of the sets identifies one or more models of the plurality of mathematical models. One of the sets comprises a transition-from set and one of the sets comprises a transition-to set. Data describing a launch date for commencing a transition from executing the model(s) in the transition-from set to executing the model(s) in the transition-to set is received. Data describing a transition period of time for the transition is received. The models in the transition-from set and in the transition-to set are executed during the transition period of time. An output of the execution of the model(s) in the transition-from set is combined with an output of the execution of the model(s) in the transition-to set. The output is stored in a database.

Abstract: Model metadata for each of a plurality of models is stored. The model metadata includes a statistical analysis technique identifier and one or more model input data identifiers. A request to execute a model is received. The request includes data identifying one of the plurality of models, and a model execution start date and end date. On the model execution start date, execution of the model associated with the model execution request is commenced. Outputs of the executed model are stored in a database. The outputs are associated with a model instance identifier, information describing a context for execution of the model, and model output type information. The outputs are retrieved, using the model instance identifier, for analysis.

Abstract: Model metadata for each of a plurality of mathematical models is stored. Data describing two sets of models is received. Each of the sets identifies one or more models of the plurality of mathematical models. One of the sets comprises a transition-from set and one of the sets comprises a transition-to set. Data describing a launch date for commencing a transition from executing the model(s) in the transition-from set to executing the model(s) in the transition-to set is received. Data describing a transition period of time for the transition is received. The models in the transition-from set and in the transition-to set are executed during the transition period of time. An output of the execution of the model(s) in the transition-from set is combined with an output of the execution of the model(s) in the transition-to set. The output is stored in a database.

Abstract: Model metadata for each of a plurality of models is stored. The model metadata includes a statistical analysis technique identifier and one or more model input data identifiers. A request to execute a model is received. The request includes data identifying one of the plurality of models, and a model execution start date and end date. On the model execution start date, execution of the model associated with the model execution request is commenced. Outputs of the executed model are stored in a database. The outputs are associated with a model instance identifier, information describing a context for execution of the model, and model output type information. The outputs are retrieved, using the model instance identifier, for analysis.

Abstract: Model metadata for each of a plurality of mathematical models is stored. Data describing two sets of models is received. Each of the sets identifies one or more models of the plurality of mathematical models. One of the sets comprises a transition-from set and one of the sets comprises a transition-to set. Data describing a launch date for commencing a transition from executing the model(s) in the transition-from set to executing the model(s) in the transition-to set is received. Data describing a transition period of time for the transition is received. The models in the transition-from set and in the transition-to set are executed during the transition period of time. An output of the execution of the model(s) in the transition-from set is combined with an output of the execution of the model(s) in the transition-to set. The output is stored in a database.

Abstract: Improved computer modeling techniques, and uses thereof, are described herein. A set of unstructured textual data is received. Certain textual data is removed from the set of unstructured data to form an initial vocabulary set of textual data. One or more bigrams are added to the vocabulary set of textual data to form a final vocabulary set of textual data. The final vocabulary set of textual data is divided into a plurality of subsets of textual data based on type. A model is trained using each of the plurality of subsets of textual data to form a plurality of trained models, each corresponding to one of the types.

Abstract: Model metadata for each of a plurality of mathematical models is stored. Data describing two sets of models is received. Each of the sets identifies one or more models of the plurality of mathematical models. One of the sets comprises a transition-from set and one of the sets comprises a transition-to set. Data describing a launch date for commencing a transition from executing the model(s) in the transition-from set to executing the model(s) in the transition-to set is received. Data describing a transition period of time for the transition is received. The models in the transition-from set and in the transition-to set are executed during the transition period of time. An output of the execution of the model(s) in the transition-from set is combined with an output of the execution of the model(s) in the transition-to set. The output is stored in a database.

Abstract: Model metadata for each of a plurality of models is stored. The model metadata includes a statistical analysis technique identifier and one or more model input data identifiers. A request to execute a model is received. The request includes data identifying one of the plurality of models, and a model execution start date and end date. On the model execution start date, execution of the model associated with the model execution request is commenced. Outputs of the executed model are stored in a database. The outputs are associated with a model instance identifier, information describing a context for execution of the model, and model output type information. The outputs are retrieved, using the model instance identifier, for analysis.

Abstract: Model metadata for each of a plurality of models is stored. The model metadata includes a statistical analysis technique identifier and one or more model input data identifiers. A request to execute a model is received. The request includes data identifying one of the plurality of models, and a model execution start date and end date. On the model execution start date, execution of the model associated with the model execution request is commenced. Outputs of the executed model are stored in a database. The outputs are associated with a model instance identifier, information describing a context for execution of the model, and model output type information. The outputs are retrieved, using the model instance identifier, for analysis.

Abstract: Model metadata for each of a plurality of mathematical models is stored. Data describing two sets of models is received. Each of the sets identifies one or more models of the plurality of mathematical models. One of the sets comprises a transition-from set and one of the sets comprises a transition-to set. Data describing a launch date for commencing a transition from executing the model(s) in the transition-from set to executing the model(s) in the transition-to set is received. Data describing a transition period of time for the transition is received. The models in the transition-from set and in the transition-to set are executed during the transition period of time. An output of the execution of the model(s) in the transition-from set is combined with an output of the execution of the model(s) in the transition-to set. The output is stored in a database.