Machine learning systems for computer generation of automated recommendation outputs

A computer system comprising:

• memory hardware including a machine learning model, historical feature vector inputs, and computer-executable instructions, wherein the historical feature vector inputs include historical profile data structures specific to multiple entities, historical structured supplemental data including medical claim data, and historical structured data for a plurality of health care plan option identifiers;
• and
• processor hardware including one or more processors configured to execute the instructions, wherein the instructions include:
• training, by the one or more processors, the machine learning model with the historical feature vector inputs to generate a recommendation output by:
• determining whether historical profile data structures are stored in a database with corresponding structured supplemental data and selected option identifiers,
• in response to a determination that historical profile data structures are stored in the database with corresponding structured supplemental data and selected option identifiers, generating the historical feature vector inputs using the historical profile data structures stored in the database,
• in response to a determination that historical profile data structures are not stored in the database with corresponding structured supplemental data and selected option identifiers, generating the historical feature vector inputs using created sample profile data structures,
• training, simultaneously or in succession using the generated historical feature input vectors, multiple machine learning models, each model being a different type of machine learning model,
• identifying one of the trained multiple machine learning models as having a highest output accuracy compared to the other trained machine learning models, and
• saving the identified machine learning model having the highest output accuracy as the machine learning model for use in generating the recommendation outputs;
• presenting, by the one or more processors, an interactive voice interface to an entity to generate audio questions and prompts based on an automatically generated survey for obtaining response data from the entity;
• using natural language processing, by the one or more processors, to classify voice survey responses of the entity;
• generating, by the one or more processors, a set of inputs specific to an entity, the set of inputs derived according to response data obtained from the entity;
• transforming, by the one or more processors, the set of inputs into a profile data structure, wherein the profile data structure includes multiple attributes, and wherein the transforming includes, for each attribute, assigning a preference according to the set of inputs;
• obtaining, by the one or more processors, structured supplemental data associated with the entity, wherein the structured supplemental data includes medical claim history data associated with the entity and at least one of demographic data fields associated with the entity, environmental data fields associated with a location of the entity, public health data fields associated with the location of the entity, and financial data fields associated with the entity;
• obtaining, by the one or more processors, a set of health care plan option identifiers from the plurality of health care plan option identifiers, wherein obtaining the set of health care plan option identifiers includes filtering the plurality of health care plan option identifiers according to option criteria specific to the entity;
• creating, by the one or more processors, a feature vector input according to the set of health care plan option identifiers, the structured supplemental data associated with the entity, and the assigned preferences of the profile data structure;
• processing, by the one or more processors using the identified machine learning model, the feature vector input to generate the recommendation output the recommendation output including at least one of the set of health care plan option identifiers having a greater match score than another of the set of health care plan option identifiers based on the structured supplemental data of the entity and the assigned preferences of the profile data structure;
• and
• transforming, by the one or more processors, a user interface based on the recommendation output, to display the recommendation output to the entity.