Machine learning models for generating executable sequences

A computerized method comprising:

• in response to designation of an entity within a data store:
• obtaining information corresponding to the designated entity from the data store, wherein the obtained information includes at least one of prescription information, medical information, exercise information, nutrition information, or lab information;
• obtaining a plurality of responses, wherein the plurality of responses is associated with a plurality of evaluation points;
• based on a set of frequencies associated with the plurality of responses:
• retraining a first machine learning model, and
• modifying the plurality of evaluation points using the retrained first machine learning model;
• and
• determining, via a second machine learning model, a health status of the designated entity based on the obtained information and the plurality of responses;
• based on the health status of the designated entity, identifying a set of states and selecting a state from the set of states;
• obtaining a set of trigger conditions for the selected state, wherein each trigger condition of the set of trigger conditions specifies a set of satisfaction criteria;
• for each trigger condition of the set of trigger conditions, determining whether the trigger condition is satisfied by evaluating the satisfaction criteria of the trigger condition based on data corresponding to the designated entity;
• selectively selecting another state from the set of states based on whether the trigger conditions of the set of trigger conditions are satisfied;
• determining an executable sequence based on the selected state;
• and
• scheduling the executable sequence for execution.

A non-transitory computer-readable medium comprising processor-executable instructions, wherein the instructions include:

• in response to designation of an entity within a data store:
• obtaining information corresponding to the designated entity from the data store, wherein the obtained information includes at least one of prescription information, medical information, exercise information, nutrition information, or lab information;
• obtaining a plurality of responses, wherein the plurality of responses is associated with a plurality of evaluation points;
• based on a set of frequencies associated with the plurality of responses:
• retraining a first machine learning model, and
• modifying the plurality of evaluation points using the retrained first machine learning model;
• and
• determining, via a second machine learning model, a health status of the designated entity based on the obtained information and the plurality of responses;
• based on the health status of the designated entity, identifying a set of states and selecting a state from the set of states;
• obtaining a set of trigger conditions for the selected state, wherein each trigger condition of the set of trigger conditions specifies a set of satisfaction criteria;
• for each trigger condition of the set of trigger conditions, determining whether the trigger condition is satisfied by evaluating the satisfaction criteria of the trigger condition based on data corresponding to the designated entity;
• selectively selecting another state from the set of states based on whether the trigger conditions of the set of trigger conditions are satisfied;
• determining an executable sequence based on the selected state;
• and
• scheduling the executable sequence for execution.