Personal Profile Generator and Recommendation Engine

1 . A computer-implemented method, comprising:

generating a plurality of clusters;

generating, for a new user, a profile;

associating the generated profile into a cluster of the plurality of clusters; and

generating, by a machine learning (ML) model, a recommendation for the user based on the associated cluster.

2 . The computer-implemented method of claim 1 wherein generating the plurality of clusters further comprises:

identifying a plurality of existing profiles, each existing profile of the plurality of existing profiles including a set of variables;

for each of the existing profiles, identifying a value for each variable of the set of variables; and

determining, by a clustering algorithm, a first existing profile and a second existing profile, of the plurality of existing profiles, have a similarity above a similarity threshold; and

generating, by the clustering algorithm, the cluster including the first existing profile and the second existing profile.

3 . The computer-implemented method of claim 2 , wherein associating the generated profile into the cluster of the plurality of clusters further comprises:

identifying a set of variables for the generated profile;

identifying a value for each variable of the set of variables for the generated profile;

based on the identified value for each variable of the set of variables for the generated profile, determining, by the clustering algorithm, the cluster of the plurality of clusters most similar to the generated profile; and

associating the generated profile into the determined cluster.

4 . The computer-implemented method of claim 3 , wherein the set of variables include variables related to at least one of age, number of symptoms, types of symptoms, severity of symptoms, ethnicity, income, geographical location, or awareness of menopause.

5 . The computer-implemented method of claim 1 , wherein generating the recommendation for the user further comprises:

determining a health outcome associated with the cluster of the plurality of clusters;

identifying an intervention that, when applied, has a likelihood of addressing the determined health outcome; and

generating, by the ML model, the recommendation for the user, the recommendation including the intervention.

6 . The computer-implemented method of claim 5 , wherein:

the determined health outcome is a stage of menopause; and

the identified intervention includes at least one of a treatment for a symptom of the stage of menopause or educational content associated with the stage of menopause.

7 . The computer-implemented method of claim 1 , further comprising:

receiving feedback indicating a result of the recommendation; and

based on the received feedback, updating the ML model.

8 . The computer-implemented method of claim 1 , further comprising:

receiving updated information from the new user;

based on the received updated information, associating the generated profile into a second cluster of the plurality of clusters, the second cluster different than the cluster; and

generating, by the ML model, a second recommendation for the user based on the associated second cluster.

9 . An apparatus comprising:

a user interface (UI);

a memory; and

a processor coupled to the memory configured to:

control the UI to present a questionnaire;

receive, via the UI, a response to the questionnaire;

generate a profile associated with the user based on the received responses to the questionnaire;

associate the generated profile into a cluster of a plurality of clusters; and

execute a machine learning (ML) model to generate a recommendation for the user based on the associated cluster.

10 . The apparatus of claim 9 , wherein the processor is further configured to:

identify a plurality of existing profiles, each existing profile of the plurality of existing profiles including a set of variables;

for each of the existing profiles, identify a value for each variable of the set of variables; and

execute a clustering algorithm to determine a first existing profile and a second existing profile, of the plurality of existing profiles, have a similarity above a similarity threshold; and

generate, by the clustering algorithm, the cluster including the first existing profile and the second existing profile.

11 . The apparatus of claim 10 , wherein, to associate the generated profile into the cluster of the plurality of clusters, the processor is further configured to:

identify a set of variables for the generated profile;

identify a value for each variable of the set of variables for the generated profile;

based on the identified value for each variable of the set of variables for the generated profile, executed the clustering algorithm to determine the cluster of the plurality of clusters most similar to the generated profile; and

associate the generated profile into the determined cluster.

12 . The apparatus of claim 11 , wherein the set of variables include variables related to at least one of age, number of symptoms, types of symptoms, severity of symptoms, ethnicity, income, geographical location, or awareness of menopause.

13 . The apparatus of claim 9 , wherein, to generate the recommendation for the user, the processor is further configured to:

determine a health outcome associated with the cluster of the plurality of clusters;

identify an intervention that, when applied, has a likelihood of addressing the determined health outcome; and

execute the ML model to generate the recommendation for the user, the recommendation including the intervention.

14 . The apparatus of claim 13 , wherein:

the determined health outcome is a stage of menopause; and

the identified intervention includes at least one of a treatment for a symptom of the stage of menopause or educational content associated with the stage of menopause.

15 . The apparatus of claim 9 , wherein the processor is further configured to:

receive updated information from the new user;

based on the received updated information, associate the generated profile into a second cluster of the plurality of clusters, the second cluster different than the cluster; and

execute the ML model to generate a second recommendation for the user based on the associated second cluster.

16 . One or more non-transitory computer readable media storing instructions that, when executed by a processor, cause the processor to:

generate a plurality of clusters, each cluster of the generated plurality of clusters including at least a stage of menopause and symptoms experienced related to menopause;

generate, for a new user, a profile, the generated profile including at least an identified user stage of menopause and user symptoms experienced related to menopause;

associate the generated profile into a cluster of the plurality of clusters; and

generating, by a machine learning (ML) model, a recommendation for the user based on the associated cluster.

17 . The one or more non-transitory computer readable media of claim 16 , further storing instructions for generating the recommendation for the user that, when executed by the processor, cause the processor to:

determine a health outcome associated with the cluster of the plurality of clusters, the determined health outcome including the user identified stage of menopause;

identify an intervention associated with the associated cluster that, when applied, has a likelihood of addressing the determined health outcome, the identified intervention including at least one of a treatment for a symptom of the stage of menopause or educational content associated with the stage of menopause; and

generating, by the ML model, the recommendation for the user, the recommendation including the intervention.

18 . The one or more non-transitory computer readable media of claim 16 , further storing instructions for generating the plurality of clusters that, when executed by the processor, cause the processor to:

identify a plurality of existing profiles, each existing profile of the plurality of existing profiles including a set of variables;

for each of the existing profiles, identify a value for each variable of the set of variables; and

determine, by a clustering algorithm, a first existing profile and a second existing profile, of the plurality of existing profiles, have a similarity above a similarity threshold; and

generate, by the clustering algorithm, the cluster including the first existing profile and the second existing profile.

19 . The one or more non-transitory computer readable media of claim 18 , further storing instructions for associating the generated profile into the cluster of the plurality of clusters that, when executed by the processor, cause the processor to:

identify a set of variables for the generated profile;

identify a value for each variable of the set of variables for the generated profile;

based on the identified value for each variable of the set of variables for the generated profile, determine, by the clustering algorithm, the cluster of the plurality of clusters most similar to the generated profile; and

associate the generated profile into the determined cluster.

20 . The one or more non-transitory computer readable media of claim 16 , further storing instructions that, when executed by the processor, cause the processor to:

receive updated information from the new user;

based on the received updated information, associate the generated profile into a second cluster of the plurality of clusters, the second cluster different than the cluster; and

generate, by the ML model, a second recommendation for the user based on the associated second cluster.