Systems and methods for transforming a user interface according to predictive models

A system for transforming a user interface according to machine learning, the system comprising:

• a first data store including a persona and a data structure associated with the persona;
• a second data store configured to store:
• at least one of a first trained machine learning model and a second trained machine learning model, and
• at least one of a first set of explanatory variables and a second set of explanatory variables;
• and
• at least one processor operatively coupled to the first data store and the second data store, wherein the at least one processor is configured by a set of instructions to:
• determine whether a first condition of a set of conditions is true for the data structure,
• in response to determining that the first condition is true for the data structure, determine whether a second condition of the set of conditions is true for the data structure,
• in response to determining that the second condition is not true for the data structure:
• build the first trained machine learning model by:
• adding a first initial parameter and the first set of explanatory variables to a first training data structure,
• determining whether a first response variable is present in the data structure,
• in response to determining that the first response variable is present in the data structure:
• adding a first value from the data structure associated with the first response variable to the first training data structure, and
• associating the first value and the first set of explanatory variables in the first training data structure, and
• training the first trained machine learning model with the first training data structure,
• input the first set of explanatory variables into the first trained machine learning model and generate a first event prediction, wherein the first event prediction includes a probability of a first event occurring within a first epoch, and
• transform the user interface according to the first event prediction,
• in response to determining that the second condition is true for the data structure, determine whether a third condition of the set of conditions is true for the data structure;
• and
• in response to determining that the third condition is true for the data structure:
• build the second trained machine learning model by:
• adding a second initial parameter and the second set of explanatory variables to a second training data structure,
• determining whether a second response variable is present in the data structure,
• in response to a determination that the second response variable is present in the data structure:
• adding a second value from the data structure associated with the second response variable to a second training data structure, and
• associating the second value and the second set of explanatory variables in the second training data structure, and
• training the second trained machine learning model with the second training data structure,
• input the second set of explanatory variables into the second trained machine learning model to generate a second event prediction, wherein the second event prediction includes a probability of a second event occurring within a second epoch, and
• transform the user interface according to the second event prediction, wherein the first event prediction is a probability of the persona transitioning from treatment with a single drug in a first class of drugs to treatment with multiple drugs in the first class of drugs within a first epoch.

A non-transitory computer-readable medium comprising executable instructions for transforming a user interface according to machine learning, wherein the executable instructions include:

• selecting a persona from a data store;
• loading, into a data processing module, a data structure associated with the selected persona;
• determining, at the data processing module, whether a first condition of a set of conditions is true for the data structure;
• in response to determining that the first condition is true for the data structure, determining whether a second condition of the set of conditions is true for the data structure;
• in response to determining that the second condition is not true for the data structure:
• building, at the data processing module, a first trained machine learning model by:
• loading, at the data processing module, a first initial parameter, a first set of explanatory variables from the data structure, a first response variable,
• adding the first initial parameter and the first set of explanatory variables to a first training data structure,
• determining whether the first response variable is present in the data structure,
• in response to determining that the first response variable is present in the data structure:
• adding a first value from the data structure associated with the first response variable to the first training data structure, and
• associating the first value and the first set of explanatory variables in the first training data structure, and
• training the first trained machine learning model with the first training data structure,
• inputting, at the data processing module, the first set of explanatory variables to the first trained machine learning model to generate a first event prediction, wherein the first event prediction includes a probability of a first event occurring within a first epoch, and
• transforming the user interface according to the selected persona and the first event prediction;
• in response to determining that the second condition is true for the data structure, determining whether a third condition of the set of conditions is true for the data structure, wherein:
• the second condition and the third condition are indicative of a second event, and
• the first event and the second event are different;
• and
• in response to determining that the third condition is true for the data structure:
• building, at the data processing module, a second trained machine learning model by:
• loading, at the data processing module, a second initial parameter, a second set of explanatory variables from the data structure, and a second response variable,
• adding the second initial parameter and the second set of explanatory variables to a second training data structure,
• determining whether the second response variable is present in the data structure,
• in response to determining that the second response variable is present in the data structure:
• adding a second value from the data structure associated with the second response variable to the second training data structure, and
• associating the second value and the second set of explanatory variables in the second training data structure, and
• training the second trained machine learning model with the second training data structure,
• inputting, at the data processing module, the second set of explanatory variables to the second trained machine learning model to generate a second event prediction, wherein the second event prediction includes a probability of a second event occurring within a second epoch, and
• transforming the user interface according to the selected persona and the second event prediction, wherein the first event prediction is a probability of the selected persona transitioning from treatment with a single drug in a first class of drugs to treatment with multiple drugs in the first class of drugs within a first epoch.