METHODS AND SYSTEMS FOR PRIORITIZING COMPREHENSIVE PROGNOSES AND GENERATING AN ASSOCIATED TREATMENT INSTRUCTION SET

1 . A system for prioritizing comprehensive prognoses and generating a treatment instruction set, the system comprising:

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

receive at least a user biological marker;

generate a classification machine-learning model using machine-learning training data wherein the machine-learning training data contains a plurality of data entries containing biological markers as inputs correlated to associated diagnostics as outputs;

determine a diagnostic using the at least a user biological marker, the classification machine-learning model, and the machine-learning training data;

rank the diagnostic, wherein ranking further comprises using a statistical machine-learning process to determine a figure of merit of the diagnostic matching the user biological marker; and

select a prognosis as a function of the figure of merit, wherein selecting the prognosis further comprises:

generating at least a treatment;

performing a simulation machine-learning process, wherein the simulation machine-learning process generates an output containing a prognosis using the at least a treatment as an input;

determining a rank for the prognosis; and

providing an instruction set that results in an optimal prognosis;

display, using a graphical user interface, the instruction set.

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

collect a pattern of biological marker data from a wearable device to generate the at least a user biological marker;

classify, using the classification machine-learning model, the at least a user biological marker data to a diagnostic profile as a function of the pattern of biological marker data.

3 . The system of claim 1 , wherein generating the at least a treatment further comprises:

retrieving a treatment using a query within a database for a treatment, wherein querying is performed as a function of a user preference;

determining a relevancy rank for the treatment in addressing the diagnostic, wherein the relevancy rank relates to the ability of the treatment to result in an optimal prognosis; and.

providing an instruction set for the treatment, wherein the instruction set is a series of instructions a user may perform to address the diagnostic.

4 . The system of claim 3 , wherein the treatment further comprises a supervised machine-learning model.

5 . The system of claim 1 , wherein generating the at least a treatment further comprises ranking a plurality of instructions contained within the instruction set using a ranking machine-learning process, wherein the ranking machine-learning process ranks the plurality of instructions as a function of a chronological ordering that results in an optimal prognosis for the at least a treatment.

6 . The system of claim 1 , wherein performing the simulation machine-learning process further comprises:

sampling at least a parameter in the biological marker associated with implementing an instruction set, wherein sampling perturbs the at least a parameter in the biological marker;

determining an effect of at least a simulated parameter in the biological marker, wherein determining further comprises calculating how the at least a parameter in the biological marker may change as a function of the at least a simulated parameter in the biological marker; and

identifying at least a simulated parameter in the biological marker that results in a ranked treatment containing an optimal prognosis from implementing the instruction set.

7 . The system of claim 6 , wherein the computing device is further configured to modify the instruction set to reflect the at least a simulated parameter.

8 . The system of claim 1 , wherein ranking the diagnostic further comprises ranking a plurality of diagnostics as a function of a ranking machine-learning process and a figure of merit of each diagnostic.

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

direct the user to update user data as a function of the instruction set, wherein directing further comprises:

displaying a notification to remind the user to follow-up;

calculating a cooperation rank, wherein the cooperation rank relates a user rank with a level of cooperation and a completion of the instruction set; and

providing, via the user device, the cooperation rank.

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

receive updated biological marker data, where updated biological marker data is more recent in time than the at least a user biological marker

compute, using the classification machine-learning model and the updated biological marker data, a numerical difference between the figure of merit and an updated figure of merit; and

determine a prognosis state as a function of the numerical difference.

11 . A method of prioritizing comprehensive prognoses and generating a treatment instruction set, the method comprising:

receiving, by a computing device, at least a user biological marker;

generating, by the computing device, a classification machine-learning model using machine-learning training data wherein the machine-learning training data contains a plurality of data entries containing biological markers as inputs correlated to associated diagnostics as outputs;

determining, by the computing device, a diagnostic using the at least a user biological marker, the classification machine-learning model, and the machine-learning training data;

ranking, by the computing device, the diagnostic, wherein ranking further comprises using a statistical machine-learning process to determine a figure of merit of the diagnostic matching the user biological marker; and

selecting, by the computing device, a prognosis as a function of the figure of merit, wherein selecting the prognosis further comprises:

generating at least a treatment;

performing a simulation machine-learning process, wherein the simulation machine-learning process generates an output containing a prognosis using the at least a treatment as an input;

determining a rank for the prognosis; and

providing an instruction set that results in an optimal prognosis;

displaying, by the computing device, using a graphical user interface, the instruction set.

12 . The method of claim 11 , wherein the at least a user biological marker further comprises:

collecting a pattern of biological marker data from a wearable device to generate the at least a user biological marker;

classifying, using the classification machine-learning model, the at least a user biological marker data to a diagnostic profile as a function of the pattern of biological marker data.

13 . The method of claim 11 , wherein generating the at least a treatment further comprises:

retrieving a treatment using a query within a database for a treatment, wherein querying is performed as a function of a user preference;

determining a relevancy rank for the treatment in addressing the diagnostic, wherein the relevancy rank relates to the ability of the treatment to result in an optimal prognosis; and.

providing an instruction set for the treatment, wherein the instruction set is a series of instructions a user may perform to address the diagnostic.

14 . The method of claim 13 , wherein the treatment further comprises a supervised machine-learning model.

15 . The method of claim 11 , wherein generating the at least a treatment further comprises ranking a plurality of instructions contained within the instruction set using a ranking machine-learning process, wherein the ranking machine-learning process ranks the plurality of instructions as a function of a chronological ordering that results in in an optimal prognosis for the at least a treatment.

16 . The method of claim 11 , wherein performing the simulation machine-learning process further comprises:

sampling at least a parameter in the biological marker associated with implementing an instruction set, wherein sampling perturbs the at least a parameter in the biological marker;

determining an effect of at least a simulated parameter in the biological marker, wherein determining further comprises calculating how at least a parameter in the biological marker may change as a function of the at least a simulated parameter in the biological marker; and

identifying at least a simulated parameter in the biological marker that results in a ranked treatment containing an optimal prognosis from implementing the instruction set.

17 . The method of claim 16 , further comprising modifying the instruction set to reflect the at least a simulated parameter.

18 . The method of claim 11 , wherein ranking the diagnostic further comprises ranking a plurality of diagnostics as a function of a ranking machine-learning process and a figure of merit of each diagnostic.

19 . The method of claim 11 further comprising:

directing the user to update user data as a function of the instruction set, wherein directing further comprises:

displaying a notification to remind the user to follow-up;

calculating a cooperation rank, wherein the cooperation rank relates a user rank with a level of cooperation and a completion of the instruction set; and

providing, via the user device, the cooperation rank.

20 . The method of claim 11 further comprising:

Receiving, updated biological marker data, where updated biological marker data is more recent in time than the at least a user biological marker;

computing, using the classification machine-learning model and the updated biological marker data, a numerical difference between the figure of merit and an updated figure of merit; and

determining, a prognosis state as a function of the numerical difference.