Apparatus and method for generating a treatment plan for salutogenesis

1. An apparatus for generating a treatment plan for salutogenesis, the apparatus comprising:

at least a processor; and

a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:

receive physiological data associated with a user and comprising a plurality of biomarkers, wherein the plurality of biomarkers comprises at least a monitoring biomarker, a diagnostic biomarker, and a predictive biomarker, each biomarker being extracted from the user chemically;

determine a concentration for each biomarker of the plurality of biomarkers;

classify each biomarker of the plurality of biomarkers to a disease condition and a treatment label as a function of the concentration, wherein the classification further comprises:

receiving treatment training data correlating each biomarker and its concentration with the disease condition and the treatment label;

training, iteratively, a treatment classifier using the treatment training data, wherein the treatment training data further comprises data correlating biomarkers related to glycocalyx degradation; and

classifying each biomarker to the disease condition and the treatment label using the treatment classifier, wherein the treatment classifier receives the plurality of biomarkers and the concentration for each biomarker as an input and outputs the disease condition and the treatment label; and

generate a treatment plan as a function of the disease condition and the treatment label.

2. The apparatus of claim 1 , wherein the plurality of biomarkers includes at least a glycocalyx degradation biomarker.

3. The apparatus of claim 2 , wherein the at least a glycocalyx degradation biomarker is measured as a function of a movement of red blood cells expressed as a perfused boundary region.

4. The apparatus of claim 1 , wherein the treatment plan includes plasma exchange.

5. The apparatus of claim 1 , wherein physiological data includes a plurality of capillary density measurements.

6. The apparatus of claim 5 , wherein the processor is further configured to:

receive health density training data correlating capillary density measurements and historical glycocalyx degradation capillary density measurements to the treatment label; and

train a health density classifier using the health density training data.

7. The apparatus of claim 5 , wherein the processor is further configured to:

classify the capillary density measurements to the treatment label; and

generate the treatment plan as a function of the treatment label.

8. The apparatus of claim 5 , wherein the plurality of capillary density measurements are measured using sublingual video microscopy.

9. The apparatus of claim 1 , wherein generating the treatment plan further comprises:

receive treatment frequency training data, wherein the treatment frequency training data correlates health condition data and frequency of treatment data to time period required to improve the health condition;

train a machine-learning process using the treatment frequency training data; and

output the treatment plan as a function of at least one health condition and the machine-learning process.

10. The apparatus of claim 1 , wherein the treatment label identifies a treatment frequency.

11. A method for generating a treatment plan for salutogenesis, the method comprising:

receiving, at a processor, physiological data associated with a user and comprising a plurality of biomarkers, wherein the plurality of biomarkers comprises at least a monitoring biomarker, a diagnostic biomarker, and a predictive biomarker, each biomarker being extracted from the user chemically;

determining, at a processor, a concentration for each biomarker of the plurality of biomarkers;

classifying, at a processor, each biomarker of the plurality of biomarkers to a disease condition and a treatment label as a function of the concentration, wherein the classification further comprises:

receiving treatment training data correlating each biomarker and its concentration with the disease condition and the treatment label;

training, iteratively, a treatment classifier using the treatment training data, wherein the treatment training data further comprises data correlating biomarkers related to glycocalyx degradation; and

classifying each biomarker to the disease condition and the treatment label using the treatment classifier, wherein the treatment classifier receives the plurality of biomarkers and the concentration for each biomarker as an input and outputs; and

generating, at a processor, a treatment plan as a function of the disease condition and the treatment label.

12. The method of claim 11 , wherein the plurality of biomarkers includes at least a glycocalyx degradation biomarker.

13. The method of claim 12 , wherein the at least a glycocalyx degradation biomarker is measured as a function of a movement of red blood cells expressed as a perfused boundary region.

14. The method of claim 11 , wherein the treatment plan includes plasma exchange.

15. The method of claim 11 , wherein physiological data includes a plurality of capillary density measurements.

16. The method of claim 15 , wherein the processor is further configured to:

receiving health density training data correlating capillary density measurements and historical glycocalyx degradation capillary density measurements to the treatment label; and

training a health density classifier using the health density training data.

17. The method of claim 15 , wherein the processor is further configured to:

classifying the capillary density measurements to the treatment label; and

generating the treatment plan as a function of the treatment label.

18. The method of claim 15 , wherein the plurality of capillary density measurements are measured using sublingual video microscopy.

19. The method of claim 11 , wherein generating a treatment plan further comprises:

receiving treatment frequency training data, wherein the treatment frequency training data correlates health condition data and frequency of treatment data to time period required to improve the health condition;

training a machine-learning process using the treatment frequency training data; and

outputting the treatment plan as a function of at least one health condition and the machine-learning process.

20. The method of claim 11 , wherein generating the treatment label further comprises identifying a treatment frequency.