Methods and systems for informed selection of prescriptive therapies

1. A system for informed selection of prescriptive therapies, the system comprising a computing device, the computing device configured to:

receive compositional training data wherein the compositional training data contains a plurality of unclassified data entries;

retrieve a user biological profile wherein the user biological profile further comprises at least a biological extraction and a prescriptive therapy indicator;

generate an unsupervised machine-learning model, said unsupervised machine-learning model trained according to the compositional training data and configured to receive the user biological profile as an input and output a selection of a therapy response model based upon the prescriptive therapy indicator; and

generate the selected therapy response model, said therapy response model comprising a trained machine-learning model configured to receive the user biological profile and a proposed prescriptive therapy from a remote device as inputs and output a prescriptive therapy label for the proposed prescriptive therapy.

2. The system of claim 1 , wherein generating the unsupervised machine-learning algorithm further comprises selecting at least an unsupervised feature as a function of the user biological profile wherein the at least an unsupervised feature further contains a prescriptive therapy treatment indicator.

3. The system of claim 2 , wherein the at least an unsupervised feature further comprises a genetic feature.

4. The system of claim 2 , wherein the at least an unsupervised feature further comprises a constitutional feature.

5. The system of claim 2 , wherein the at least an unsupervised feature further comprises an ecological feature.

6. The system of claim 1 , wherein generating the unsupervised machine-learning algorithm further comprises generating a clustering algorithm.

7. The system of claim 6 , wherein generating the clustering algorithm further comprises generating a k-means clustering algorithm.

8. The system of claim 1 , wherein the computing device is further configured to:

receive contraindication training data wherein the contraindication training data contains a plurality of data entries containing prescriptive therapies and correlated contraindications;

retrieve user current therapies;

generate a machine-learning model using the contraindication training data wherein the machine-learning model utilizes prescriptive therapies as an input and outputs contraindications;

evaluate the output contraindications; and

generate a potential prescriptive therapy label as a function of evaluating the output contraindications.

9. The system of claim 1 , wherein the computing device is further configured to:

receive kinetic training data wherein the kinetic training data contains a plurality of data entries containing kinetic data and correlated prescriptive therapies;

retrieve an element of user kinetic data;

generate a machine-learning model using the kinetic training data wherein the machine-learning model utilizes the element of user kinetic data as an input and outputs compatible prescriptive therapies; and

compare output compatible prescriptive therapies to the proposed prescriptive therapy.

10. The system of claim 1 , wherein the computing device is further configured to:

receive genetic training data wherein the genetic training data contains a plurality of data entries containing genetic data and correlated prescriptive therapies;

retrieve an element of user genetic data;

generate a machine-learning model using the genetic training data wherein the machine-learning model utilizes the element of user genetic data as an input and outputs compatible prescriptive therapies; and

identify a prescriptive therapy label as a function of generating the machine-learning model.

11. A method of informed selection of prescriptive therapies, the method comprising:

receiving by a computing device compositional training data wherein the compositional training data contains a plurality of unclassified data entries;

retrieving by the computing device a user biological profile wherein the user biological profile further comprises at least a biological extraction and a prescriptive therapy indicator;

generating by the computing device an unsupervised machine-learning model, said unsupervised machine-learning model trained according to the compositional training data and configured to receive the user biological profile as an input and output a selection of a therapy response model based upon the prescriptive therapy indicator; and

generating by the computing device the selected therapy response model, said therapy response model comprising a trained machine-learning model configured to receive the user biological profile and a proposed prescriptive therapy from a remote device as inputs and output a prescriptive therapy label for the proposed prescriptive therapy.

12. The method of claim 11 , wherein generating the unsupervised machine-learning algorithm further comprises selecting at least an unsupervised feature as a function of the user biological profile wherein the at least an unsupervised feature further contains a prescriptive therapy treatment indicator.

13. The method of claim 12 , wherein the at least an unsupervised feature further comprises a genetic feature.

14. The method of claim 12 , wherein the at least an unsupervised feature further comprises a constitutional feature.

15. The method of claim 12 , wherein the at least an unsupervised feature further comprises an ecological feature.

16. The method of claim 11 , wherein generating the unsupervised machine-learning algorithm further comprises generating a clustering algorithm.

17. The method of claim 16 , wherein generating the clustering algorithm further comprises generating a k-means clustering algorithm.

18. The method of claim 11 , further comprising:

receiving contraindication training data wherein the contraindication training data contains a plurality of data entries containing prescriptive therapies and correlated contraindications;

retrieving user current therapies;

generating a machine-learning model using the contraindication training data wherein the machine-learning model utilizes prescriptive therapies as an input and outputs contraindications;

evaluating the output contraindications; and

generating a prescriptive therapy label as a function of evaluating the output contraindications.

19. The method of claim 11 , further comprising:

receiving kinetic training data wherein the kinetic training data contains a plurality of data entries containing kinetic data and correlated prescriptive therapies;

retrieving an element of user kinetic data;

generating a machine-learning model using the kinetic training data wherein the machine-learning model utilizes the element of user kinetic data as an input and outputs compatible prescriptive therapies; and

comparing output compatible prescriptive therapies to proposed prescriptive therapy.

20. The method of claim 11 , further comprising:

receiving genetic training data wherein the genetic training data contains a plurality of data entries containing genetic data and correlated prescriptive therapies;

retrieving an element of user genetic data;

generating a machine-learning model using the genetic training data wherein the machine-learning model utilizes the element of user genetic data as an input and outputs compatible prescriptive therapies; and

identifying a prescriptive therapy label as a function of generating the machine-learning model.