SYSTEMS AND METHODS FOR GENERATING A NUTRITIVE PLAN TO MANAGE A UROLOGICAL DISORDER

1 . A system for generating a nutritive plan to manage a urological disorder, the system comprising a computing device, wherein the computing device is configured to:

receive an input comprising physiological data;

extract at least one disease marker related to at least one urological disorder;

generate a disease marker classifier, wherein generating the disease marker classifier comprises:

receiving disease marker training data correlating disease markers related to urological disorders to a urological disorder label; and

training the disease marker classifier using the disease marker training data;

classify, using the disease marker classifier, the at least disease marker to a urological disorder label; and

generate a nutritive plan as a function of the urological disorder label.

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

receive disease predictor training data, wherein the disease predictor training data correlates disease markers related to urological disorders and urological disorder labels with disease predictor scores;

train, using the disease predictor training data, a machine-learning process; and

generate, for each disease marker of a plurality of disease markers, a disease predictor score as a function of the machine-learning process and each respective disease marker of the plurality of disease markers.

3 . The system of claim 2 , wherein the computing device is further configured to:

identify a disease marker of the plurality of disease markers having a highest disease predictor score; and

generate the nutritive plan as a function of the identification.

4 . The system of claim 1 , wherein the physiological data includes results of a prostate-specific antigen test.

5 . The system of claim 1 , wherein the at least one disease marker comprises a diagnostic disease marker.

6 . The system of claim 1 , wherein generating the nutritive plan further comprises:

receiving nutritive plan training data, wherein the nutritive plan training data correlates nutritive plans to nutritive plans with a historical ameliorative or preventive effect on urological disorders;

training a machine-learning process using the nutritive plan training data; and

outputting the nutritive plan as a function of the urological disorder and the machine-learning process.

7 . The system of claim 6 , wherein outputting the nutritive plan further comprises outputting a message independent of a presence of the nutritive plan.

8 . The system of claim 1 , wherein the computing device is further configured to output the nutritive plan to a user device.

9 . The system of claim 1 , wherein the nutritive plan manages a plurality of disorders.

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

receive a second input;

reclassify the at least one disease marker from the second input to a urological disorder label; and

update the nutritive plan as a function of the second input.

11 . A method for generating a nutritive plan to manage a urological disorder, the method comprising:

receiving, by a computing device, an input comprising physiological data;

extracting, by the computing device, at least one disease marker related to at least one urological disorder;

generating, by the computing device, a disease marker classifier, wherein generating the disease marker classifier comprises:

receiving disease marker training data correlating disease markers related to urological disorders to a urological disorder label; and

training the disease marker classifier using the disease marker training data;

classifying, by the computing device and using the disease marker classifier, the at least disease marker to a urological disorder label; and

generating a nutritive plan as a function of the urological disorder label.

12 . The method of claim 11 , further comprising:

receiving disease predictor training data, wherein the disease predictor training data correlates disease markers related to urological disorders and urological disorder labels with disease predictor scores;

training, using the disease predictor training data, a machine-learning process; and

generating, for each disease marker of a plurality of disease markers, a disease predictor score as a function of the machine-learning process and each respective disease marker of the plurality of disease markers.

13 . The method of claim 12 , further comprising:

identifying a disease marker of the plurality of disease markers having a highest disease predictor score; and

generating the nutritive plan as a function of the identification.

14 . The method of claim 11 , wherein the physiological data includes results of a prostate-specific antigen test.

15 . The method of claim 11 , wherein the at least one disease marker comprises a diagnostic disease marker.

16 . The method of claim 11 , wherein generating the nutritive plan further comprises:

receiving nutritive plan training data, wherein the nutritive plan training data correlates nutritive plans to nutritive plans with a historical ameliorative or preventive effect on urological disorders;

training a machine-learning process using the nutritive plan training data; and

outputting the nutritive plan as a function of the urological disorder and the machine-learning process.

17 . The method of claim 16 , wherein outputting the nutritive plan further comprises outputting a message independent of a presence of the nutritive plan.

18 . The method of claim 11 , further comprising outputting the nutritive plan to a user device.

19 . The method of claim 11 , wherein the nutritive plan manages a plurality of disorders.

20 . The method of claim 11 , further comprising:

receiving a second input;

reclassifying the at least one disease marker from the second input to a urological disorder label; and

updating the nutritive plan as a function of the second input.