Systems and methods for selecting an intervention based on effective age

1. A system for selecting an intervention based on effective age, the system comprising:

at least a server, the at least a server designed and configured to:

record a user blood test indicating at least a measure of user endocrine function;

generate a first supervised machine-learning model, wherein:

the first supervised machine-learning model comprises a trained supervised machine-learning model trained by training data correlating at least a measure of user endocrine function to variances between actuarial life expectancy datum and actual mortality dates;

the first supervised machine-learning model is configured to receive the at least a measure of endocrine function as inputs; and

the first supervised machine-learning model is configured to output a user endocrinal age factor comprising a first variance between actuarial life expectancy and a projected actual mortality date;

record a user genetic sample, wherein the user genetic sample includes a measure of user telomere length;

generate a second supervised machine-learning model, wherein:

the second supervised machine-learning model comprises a trained supervised machine-learning model trained by training data correlating telomere length to variances between actuarial life expectancy datum actual mortality dates;

the second supervised machine-learning model is configured to receive the measure of user telomere length as a input; and

the second supervised machine-learning is configured to output a user telomeric age factor model comprising a second variance between actuarial life expectancy and a projected actual mortality date;

generate a third supervised machine-learning model, wherein:

the third supervised machine-learning model comprises a trained supervised machine-learning model trained by training data correlating user negative habit to variances between actuarial life expectancy datum and actual mortality dates;

the third supervised machine-learning model is configured to receive a user negative habit as an input; and

the third supervised machine-learning model is configured to output a user negative habit factor comprising a variance between actuarial life expectancy and projected actual mortality date;

calculate at least a user effective age as a function of the first supervised machine-learning model, the second supervised machine-learning model, and the third supervised machine-learning model, wherein calculating the at least a user effective age further comprises multiplying a user chronological age by the user telomeric age factor, the user endocrinal age factor, and the user negative habit factor;

derive a user health quality vector, wherein the user health quality vector further comprises a plurality of health vector entries including:

an effective age reduction value indicating a degree of importance of effective age reduction; and

at least a life quality objective value indicating a numerical measure of a user life quality priority;

generate a plurality of interventions, wherein

each intervention of the plurality of interventions includes an intervention vector having a plurality of intervention vector entries;

the plurality of intervention vector entries includes a vector entry corresponding to each health vector entry of the plurality of health vector entries; and

each intervention vector entry indicates a degree of impact on a factor represented by a health vector entry; and

select an intervention from the plurality of interventions, wherein selecting the intervention further comprises:

generating a cost function of the plurality of interventions and the user health quality vector;

minimizing the cost function; and

selecting the intervention from the plurality of interventions as a function of minimizing the cost function.

2. The system of claim 1 , wherein the at least a server is further configured to:

receive training data correlating one or more changes in endocrinal measures to variances between actuarial life expectancy datum and actual mortality dates;

generate, using a supervised machine-learning process, an endocrinal age factor model that receives one or more changes in endocrinal measures as inputs and produces an output representing a variance between actuarial life expectancy and a projected actual mortality date;

calculate at least a change in an endocrinal measure using the at least a measure of endocrine function; and

determine the user endocrinal age factor using the at least a change in the endocrine measure and the endocrinal age factor model.

3. The system of claim 1 , wherein the at least a server is further configured to:

identify, using a first unsupervised machine learning process, a correlation between at least a biomarker and telomere length;

receive training data correlating the at last a biomarker to variances between actuarial life expectancy datum and actual mortality dates; and

generate the training data correlating telomere length to variances between actuarial life expectancy datum and actual mortality dates using the training data correlating the at last a biomarker to variances between actuarial life expectancy datum and actual mortality dates and the correlation of between the at least a biomarker and telomere length.

4. The system of claim 1 , wherein the at least a server is further configured to:

receive a training set correlating blood tests to negative habits;

generate a negative habit identifier model using a supervised machine-learning algorithm and said training set;

produce a negative habit output from the negative habit identifier model using a user blood test; and

identify the user negative habit as a function of the negative habit output.

5. The system of claim 1 , wherein deriving the user health quality vector further comprises:

generating a default vector;

displaying the default vector to the user;

receiving a user command modifying the default vector; and

deriving the user health quality vector using the default vector and the user command.

6. The system of claim 5 , wherein generating the default vector further comprises:

receiving a training set correlating a cohort of individual information to individual health quality vectors;

generating a set of user data regarding the user; and

deriving the default vector from the training set as a function of the set of user data using a K-nearest neighbors algorithm.

7. The system of claim 1 , wherein the selected intervention comprises a reduction in consumption of a particular nutrient.

8. The system of claim 1 , wherein the selected intervention comprises a specific meal.

9. A method of selecting an intervention based on effective age, the method comprising:

recording, by at least a server, a user blood test indicating at least a measure of user endocrine function;

generating, by the at least a server, a first supervised machine-learning model, wherein:

the first supervised machine-learning model comprises a trained supervised machine-learning model trained by training data correlating at least a measure of user endocrine function to variances between actuarial life expectancy datum and actual mortality dates;

the first supervised machine-learning model is configured to receive the at least a measure of endocrine function as inputs; and

the first supervised machine-learning model is configured to output a user endocrinal age factor comprising a first variance between actuarial life expectancy and a projected actual mortality date;

recording, by the at least a server, a user genetic sample, wherein the user genetic sample includes a measure of user telomere length;

generating, by the at least a server, a second supervised machine-learning model, wherein:

the second supervised machine-learning model comprises a trained supervised machine-learning model trained by training data correlating telomere length to variances between actuarial life expectancy datum actual mortality dates;

the second supervised machine-learning model is configured to receive the measure of user telomere length as a input; and

the second supervised machine-learning is configured to output a user telomeric age factor model comprising a second variance between actuarial life expectancy and a projected actual mortality date;

generating, by the at least a server, a third supervised machine-learning model, wherein:

the third supervised machine-learning model comprises a trained supervised machine-learning model trained by training data correlating user negative habit to variances between actuarial life expectancy datum and actual mortality dates;

the third supervised machine-learning model is configured to receive a user negative habit as an input; and

the third supervised machine-learning model is configured to output a user negative habit factor comprising a variance between actuarial life expectancy and projected actual mortality date;

calculating, by the at least a server, at least a user effective age as a function of the first supervised machine-learning model, the second supervised machine-learning model, and the third supervised machine-learning model, wherein calculating the at least a user effective age further comprises multiplying a user chronological age by the user telomeric age factor, the user endocrinal age factor, and the user negative habit factor;

deriving, by the at least a server, a user health quality vector, wherein the user health quality vector further comprises a plurality of health vector entries including:

an effective age reduction value indicating a degree of importance of effective age reduction; and

at least a life quality objective value indicating a numerical measure of a user life quality priority;

generating, by the at least a server, a plurality of interventions, wherein

each intervention of the plurality of interventions includes an intervention vector having a plurality of intervention vector entries;

the plurality of intervention vector entries includes a vector entry corresponding to each health vector entry of the plurality of health vector entries; and

each intervention vector entry indicates a degree of impact on a factor represented by a health vector entry; and

selecting, by the at least a server, an intervention from the plurality of interventions, wherein selecting the intervention further comprises:

generating a cost function of the plurality of interventions and the user health quality vector;

minimizing the cost function; and

selecting the intervention from the plurality of interventions as a function of minimizing the cost function.

10. The method of claim 9 , further comprising:

receiving training data correlating one or more changes in endocrinal measures to variances between actuarial life expectancy datum and actual mortality dates;

generating, using a supervised machine-learning process, an endocrinal age factor model that receives one or more changes in endocrinal measures as inputs and produces an output representing a variance between actuarial life expectancy and a projected actual mortality date;

calculating at least a change in an endocrinal measure using the at least a measure of endocrine function; and

determining the user endocrinal age factor using the at least a change in the endocrine measure and the endocrinal age factor model.

11. The method of claim 9 further comprising:

identifying, using a first unsupervised machine learning process, a correlation between at least a biomarker and telomere length;

receiving training data correlating the at last a biomarker to variances between actuarial life expectancy datum and actual mortality dates; and

generating the training data correlating telomere length to variances between actuarial life expectancy datum and actual mortality dates using the training data correlating the at last a biomarker to variances between actuarial life expectancy datum and actual mortality dates and the correlation of between the at least a biomarker and telomere length.

12. The method of claim 9 , further comprising:

receiving a training set correlating blood tests to negative habits;

generating a negative habit identifier model using a supervised machine-learning algorithm and the training set;

producing a negative habit output from the negative habit identifier model using a user blood test; and

identifying the user negative habit as a function of the negative habit output.

13. The method of claim 9 , wherein deriving the user health quality vector further comprises:

generating a default vector;

displaying the default vector to the user;

receiving a user command modifying the default vector; and

deriving the user health quality vector using the default vector and the user command.

14. The method of claim 13 , wherein generating the default vector further comprises:

receiving a training set correlating a cohort of individual information to individual health quality vectors;

generating a set of user data regarding the user; and

deriving the default vector from the training set as a function of the set of user data using a K-nearest neighbors algorithm.

15. The method of claim 9 , wherein the selected intervention comprises a specific exercise.

16. The method of claim 9 , wherein the selected intervention comprises a number of sleep hours.