Methods and systems for customizing treatments

1. A system for customizing treatments, the system comprising:

a computing device, the computing device designed and configured to:

record a user biological extraction containing an element of user physiological data;

receive, from a remote device operated by a user, an implementation factor;

calculate a condition state label as a function of the user physiological data;

select a treatment training set according to the condition state label, wherein the treatment training set further comprises a plurality of condition state labels and a plurality of correlated treatments;

generate a treatment model, using a second machine-learning algorithm and the treatment training set, wherein the treatment model utilizes condition state labels as inputs and outputs treatments, wherein generating the treatment model further comprises:

calculating a treatment category selector as a function of the implementation factor, wherein the implementation factor is a factor that indicates a user preference pertaining to different treatment practices; and

output a treatment utilizing the treatment model.

2. The system of claim 1 , wherein the computing device is further configured to calculate a current condition state progression indicator.

3. The system of claim 2 , wherein calculating the current condition state progression indicator comprises generating a progression model, wherein the progression model comprises a second machine-learning model trained by progression training data comprising a plurality of physiological data sets and a plurality of correlated progression indicators and wherein the progression model is configured to receive the element of user physiological data as an input and output a current condition state progression indicator.

4. The system of claim 3 , wherein the computing device is further configured to receive, from a remote device, a description of a current condition state, wherein the condition state model is further configured to receive the description of a current condition state as an input and output a current condition state progression indicator.

5. The system of claim 1 , wherein recording the user biological extraction comprises receiving a plurality of responses to a questionnaire.

6. The system of claim 1 , wherein calculating the treatment category selector further comprises multiplying an approach factor by the implementation factor and the corrective factor.

7. The system of claim 1 , wherein outputting the treatment comprises generating a plurality of treatments using the treatment model.

8. The system of claim 7 , wherein outputting the treatment further comprises:

receiving a plurality of user variables from the remote device operated by the user;

generating a loss function utilizing the user variables;

minimizing the loss function;

selecting a selected treatment from the plurality of treatments as a function of minimizing the loss function; and

outputting the selected treatment.

9. The system of claim 1 , wherein the user biological extraction comprises a glucose measurement.

10. The system of claim 1 , wherein the implementation factor indicates that the user prefers the prescriptive treatment or the fitness treatment over a medical procedure.

11. A method for customizing treatments, the method comprising:

recording, by a computing device, a user biological extraction containing an element of user physiological data;

receiving, by the computing device, from a remote device operated by a user, an implementation factor;

calculating, by the computing device, a condition state label as a function of the user physiological data;

selecting, by the computing device, a treatment training set according to the condition state label, wherein the treatment training set further comprises a plurality of condition state labels and a plurality of correlated treatments;

generating, by the computing device, a treatment model, using a second machine-learning algorithm and the treatment training set, wherein the treatment model utilizes condition state labels as inputs and outputs treatments, wherein generating the treatment model further comprises:

calculating a treatment category selector as a function of the implementation factor, wherein the implementation factor is a factor that indicates a user preference pertaining to different treatment practices; and

outputting, by the computing device, a treatment utilizing the treatment model.

12. The method of claim 11 , further comprising calculating, by the computing device, a current condition state progression indicator.

13. The method of claim 12 , wherein calculating the current condition state progression indicator comprises generating a progression model, wherein the progression model comprises a second machine-learning model trained by progression training data comprising a plurality of physiological data sets and a plurality of correlated progression indicators and wherein the progression model is configured to receive the element of user physiological data as an input and output a current condition state progression indicator.

14. The method of claim 13 , further comprising receiving, from a remote device and by the computing device, a description of a current condition state, wherein the condition state model is further configured to receive the description of a current condition state as an input and output a current condition state progression indicator.

15. The method of claim 11 , wherein recording the user biological extraction comprises receiving a plurality of responses to a questionnaire.

16. The method of claim 11 , wherein calculating the treatment category selector further comprises multiplying an approach factor by the implementation factor and the corrective factor.

17. The method of claim 11 , wherein outputting the treatment comprises generating a plurality of treatments using the treatment model.

18. The method of claim 17 , wherein outputting the treatment further comprises:

receiving a plurality of user variables from the remote device operated by the user;

generating a loss function utilizing the user variables;

minimizing the loss function;

selecting a selected treatment from the plurality of treatments as a function of minimizing the loss function; and

outputting the selected treatment.

19. The method of claim 11 , wherein the user biological extraction comprises a glucose measurement.

20. The method of claim 11 , wherein the implementation factor indicates that the user prefers the prescriptive treatment or the fitness treatment over a medical procedure.