Systems and methods for multi-domain data segmentation, automatic hypotheses generation and outcome optimization

A non-transitory computer readable medium including instructions that are executable by one or more processors to cause a system to perform a method for multi-domain data segmentation, and automatically generating and refining hypotheses, the method comprising:

• receiving multi-domain data from a plurality of data sources;
• normalizing the multi-domain data;
• identifying a trigger event based on the normalized data;
• generating an episode based on a segmentation of the normalized data and the trigger event;
• identifying at least one set of observations associated with the episode based on the normalized data and one or more relevancy metrics, whereas the one or more relevancy metrics comprises a similarity metric based on the trigger event;
• iteratively performing operations until a threshold value has been reached, wherein the operations comprise:
• generating a hypothesis based on a subset of observational features using machine learning,
• generating a measure of one or more outcomes based on one or more outcome metrics, wherein the one or more outcomes metrics comprise a provider match, a concierge referral, monetary savings, reduction in expenditure, or other product metrics, and
• validating the hypothesis based on the generated measure;
• identifying one or more driving factors for the one or more outcomes using an optimal hypothesis;
• and
• using the at least one set of observations and the one or more driving factors, generating a machine learning model for outputting individualized healthcare recommendations for a user.

A non-transitory computer readable medium including instructions that are executable by one or more processors to cause a system to perform a method for multi-domain data segmentation, and automatically generating and refining hypotheses, the method comprising:

• receiving multi-domain data from a plurality of data sources;
• normalizing the multi-domain data;
• identifying a trigger event based on the normalized data;
• generating an episode based on a segmentation of the normalized data and the trigger event;
• identifying at least one set of observations associated with the episode based on the normalized data and one or more relevancy metrics, whereas the one or more relevancy metrics comprises a similarity metric based on the trigger event;
• iteratively performing operations until a threshold value has been reached, wherein the operations comprise:
• generating a hypothesis based on a subset of observational features using machine learning,
• generating a measure of one or more outcomes based on one or more outcome metrics, and
• validating the hypothesis based on the generated measure;
• identifying one or more driving factors for the one or more outcomes using an optimal hypothesis,
• generating a set of recommendations based on the identified set of driving factors,
• generating a machine learning model for outputting individualized healthcare recommendations for a user;
• and
• outputting the recommendations through a set of outflow channels comprising customers, product and service staff, user interfaces, or digital agents.

A method utilized by a system for multi-domain data segmentation, automatic hypotheses generation and refinement, the method comprising:

• receiving multi-domain data from a plurality of data sources;
• normalizing the multi-domain data;
• identifying a trigger event based on the normalized data;
• generating an episode based on a segmentation of the normalized data and the trigger event;
• identifying at least one set of observations associated with the episode based on the normalized data and one or more relevancy metrics, whereas the one or more relevancy metrics comprises a similarity metric based on the trigger event;
• iteratively performing operations until a threshold value has been reached, wherein the operations comprise:
• generating a hypothesis based on a subset of observational features using machine learning,
• generating a measure of one or more outcomes based on one or more outcome metrics, wherein the one or more outcomes metrics comprise a provider match, a concierge referral, monetary savings, reduction in expenditure, or other product metrics, and
• validating the hypothesis based on the generated measure;
• identifying one or more driving factors for the one or more outcomes using an optimal hypothesis, and
• using the at least one set of observations and the one or more driving factors, generating a machine learning model for outputting individualized healthcare recommendations for a user.

A method utilized by a system for multi-domain data segmentation, automatic hypotheses generation and refinement, the method comprising:

• receiving multi-domain data from a plurality of data sources;
• normalizing the multi-domain data;
• identifying a trigger event based on the normalized data;
• generating an episode based on a segmentation of the normalized data and the trigger event;
• identifying at least one set of observations associated with the episode based on the normalized data and one or more relevancy metrics, whereas the one or more relevancy metrics comprises a similarity metric based on the trigger event;
• iteratively performing operations until a threshold value has been reached, wherein the operations comprise:
• generating a hypothesis based on a subset of observational features using machine learning,
• generating a measure of one or more outcomes based on one or more outcome metrics, and
• validating the hypothesis based on the generated measure;
• identifying one or more driving factors for the one or more outcomes using an optimal hypothesis,
• using the at least one set of observations and the one or more driving factors, generating a machine learning model for outputting individualized healthcare recommendations for a user;
• generating a set of recommendations using the machine learning model,
• outputting the recommendations through a set of outflow channels comprising customers, product and service staff, user interfaces, or digital agents.