Methods and systems for generating a descriptor trail using artificial intelligence

1. A system for generating a descriptor trail using artificial intelligence the system comprising:

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

receive at least a biological extraction;

generate a prognostic output as a function of the at least a biological extraction, wherein the prognostic output is selected from a plurality of prognostic outputs, wherein generating the prognostic output further comprises:

selecting a prognostic machine-learning process as a function of the at least a biological extraction;

recording the selected prognostic machine-learning process in a descriptor trail data structure; and

generating the prognostic output using the selected prognostic machine-learning process as a function of the at least a biological extraction;

generate an ameliorative output as a function of the prognostic output, wherein the ameliorative output is selected from a plurality of ameliorative outputs, wherein generating the ameliorative output further comprises:

selecting an ameliorative machine-learning process as a function of the prognostic output;

recording the selected ameliorative machine-learning process in the descriptor trail data structure; and

generating the ameliorative output using the selected ameliorative machine-learning process as a function of the prognostic output; and

a descriptor generator module operating on the at least a server wherein the descriptor generator module is designed and configured to generate at least a descriptor trail from the descriptor trail data structure wherein the at least a descriptor trail further comprises at least an element of diagnostic data, wherein the diagnostic data comprises a confidence interval associated with the prognostic output; and

select a lazy-learning process as a function of the at least a biological extraction;

record the lazy-learning process in the descriptor trail data structure; and

generate the prognostic output and the ameliorative output using the lazy-learning process as a function of the at least a biological extraction.

2. The system of claim 1 , wherein:

the at least a biological extraction further comprises at least a fluid sample; and

the at least a server is further configured to classify the at least a biological extraction wherein classifying the at least a biological extraction further comprises:

comparing the at least a biological extraction to at least a biological standard level; and

generating at least a biological extraction classifier label.

3. The system of claim 2 , wherein generating the at least a biological extraction classifier label further comprises matching the at least a biological extraction with at least a category of physiological state data received from at least an expert.

4. The system of claim 2 , wherein selecting the prognostic machine-learning process as a function of the at least a biological extraction further comprises selecting at least a first training set as a function of the at least a biological extraction classifier label.

5. The system of claim 1 , wherein selecting the prognostic machine-learning process further comprises selecting at least a first training set as a function of at least a physically extracted sample contained within the at least a biological extraction.

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

generate the plurality of prognostic outputs each containing a ranked prognostic probability score;

record the plurality of prognostic outputs in the descriptor trail data structure; and

generate the prognostic output as a function of the ranked prognostic probability score.

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

generate a plurality of ameliorative outputs each containing a prognostic improvement score correlated to at least a prognostic output as a function of the at least a prognostic output;

record the plurality of ameliorative outputs in the descriptor trail data structure; and

generate the ameliorative output as a function of the prognostic improvement score.

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

select a lazy-learning process as a function of the at least a biological extraction;

record the lazy-learning process in the descriptor trail data structure; and

generate the ameliorative output using the lazy-learning process as a function of the at least a biological extraction.

9. The system of claim 1 , wherein the descriptor generator module is further configured to:

receive at least an advisor filter input containing at least a diagnostic data selection;

generate the at least a descriptor trail as a function of the at least an advisor filter; and

transmit the at least a descriptor trail to at least an advisor client device.

10. A method of generating a descriptor trail using artificial intelligence the method comprising:

receiving by at least a server at least a biological extraction;

generating by the at least a server a prognostic output as a function of the at least a biological extraction, wherein the prognostic output is selected from a plurality of prognostic outputs, wherein generating the prognostic output further comprises:

selecting a prognostic machine-learning process as a function of the at least a biological extraction;

recording the selected prognostic machine-learning process in a descriptor trail data structure; and

generating the prognostic output using the selected prognostic machine-learning process as a function of the at least a biological extraction;

generating by the at least a server an ameliorative output as a function of the prognostic output, wherein the ameliorative output is selected from a plurality of ameliorative outputs, wherein generating the ameliorative output further comprises:

selecting an ameliorative machine-learning process as a function of the prognostic output;

recording the selected ameliorative machine-learning process in the descriptor trail data structure; and

generating the ameliorative output using the selected ameliorative machine-learning process as a function of the prognostic output; and

generating by the at least a server at least a descriptor trail from the descriptor trail data structure wherein the at least a descriptor trail further comprises at least an element of diagnostic data, wherein the diagnostic data comprises a confidence interval associated with the prognostic output; and

selecting a lazy-learning process as a function of the at least a biological extraction;

recording the lazy-learning process in the descriptor trail data structure; and

generating the prognostic output and the ameliorative output using the lazy-learning process as a function of the at least a biological extraction.

11. The method of claim 10 , wherein receiving at least a biological extraction further comprises:

receiving at least a at least a fluid sample; and

classifying the at least a biological extraction wherein classifying the at least a biological extraction further comprises:

comparing the at least a biological extraction to at least a biological standard level; and

generating at least a biological extraction classifier label.

12. The method of claim 11 , wherein generating the at least a biological extraction classifier label further comprises matching the at least a biological extraction with at least a category of physiological state data received from at least an expert.

13. The method of claim 11 , wherein selecting a prognostic machine-learning process further comprises selecting at least a first training set as a function of the at least a biological extraction classifier label.

14. The method of claim 10 , wherein selecting a prognostic machine-learning process further comprises selecting at least a first training set as a function of at least a physically extracted sample contained within the at least a biological extraction.

15. The system of claim 10 , wherein generating the prognostic output further comprises:

generating a plurality of prognostic outputs each containing a ranked prognostic probability score;

recording the plurality of prognostic outputs in the descriptor trail data structure; and

generating the prognostic output as a function of the ranked prognostic probability score.

16. The method of claim 10 , wherein generating an ameliorative output further comprises:

generating a plurality of ameliorative outputs each containing a prognostic improvement score correlated to at least a prognostic output as a function of the at least a prognostic output;

recording the plurality of ameliorative outputs in the descriptor trail data structure; and

generating the ameliorative output as a function of the prognostic improvement score.

17. The method of claim 10 , wherein generating an ameliorative output further comprises:

selecting a lazy-learning process as a function of the at least a biological extraction;

recording the lazy-learning process in the descriptor trail data structure; and

generating the ameliorative output using the lazy-learning process as a function of the at least a biological extraction.

18. The method of claim 10 , wherein generating at least a descriptor trail further comprises:

receiving at least an advisor filter input containing at least a diagnostic data selection;

generating the at least a descriptor trail as a function of the at least an advisor filter; and

transmitting the at least a descriptor trail to at least an advisor client device.