Methods and systems for identifying a causal link

1. A system for identifying a causal link including a computing device wherein the computing device further comprises one or more network interfaces and one or more processors, the system comprising:

a diagnostic generator module operating on the computing device, the diagnostic generator module designed and configured to:

receive a first user symptom datum from a user client device associated with a user wherein the first user symptom datum includes a current medical indicator;

receive diagnostic training data from a machine-learning database correlating symptom data to prognostic data; and

generate, using a supervised machine-learning process, a diagnostic model that receives the first user symptom datum as an input and produces an output containing a first prognosis;

a prognostic chaining module operating on the computing device, the prognostic chaining module designed and configured to:

receive an expert input dataset from an expert knowledge database wherein the expert input dataset comprises an expert journal article identifying prognostic data correlated to causal link data;

receive the first user symptom datum and the first prognosis from the diagnostic generator module;

generate a gaussian mixture clustering model utilizing the expert input dataset and the first prognosis and outputting a defined number of clusters; and

identify a first causal link associated with the user chained to the first prognosis as a function of generating the gaussian mixture clustering model; and

a causal link module operating on the computing device, the causal link module designed and configured to:

receive from the prognostic chaining module the first prognosis chained to the first causal link associated with the user, wherein the first causal link identifies a root cause of the first prognosis;

receive from the prognostic chaining module a second prognosis chained to a second causal link associated with the user, wherein the second causal link identifies a root cause of the second prognosis; and

evaluate the first causal link and the second causal link to calculate a degree of similarity between the first causal link and the second causal link, wherein evaluating the first causal link and the second causal link comprises generating a recommendation for further testing as a function of the degree of similarity and displaying the recommendation for further testing at an advisor client device.

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

receive a first user blood test indicating a measure of user genetic data;

receive genetic training data from a machine-learning database correlating genetic data to prognostic data; and

generate, using a supervised machine-learning process, a genetic model that receives the first user blood test indicating the measure of user genetic data as an input and produces an output containing a first prognosis.

3. The system of claim 1 , wherein generating the gaussian mixture clustering model further comprises:

selecting a defined number of clustering groups;

generating a cluster label for each of the defined number of clustering groups;

assigning the first prognosis to a labeled cluster group; and

identifying a first causal link contained within the labeled cluster group.

4. The system of claim 1 , wherein receiving a second prognosis chained to a second causal link further comprises:

receiving a second user symptom datum from a user client device wherein the second user symptom datum includes a current medical indicator;

receiving diagnostic training data from a machine-learning database correlating symptom data to prognostic data;

generating, using a supervised machine-learning process a diagnostic model that receives the second user symptom datum as an input and produces an output containing a second prognosis; and

identifying a second causal link chained to the second prognosis as a function of generating a gaussian mixture clustering model.

5. The system of claim 1 , wherein the causal link module is further configured to select the first causal link by determining that the first causal link matches the second causal link.

6. The system of claim 1 , wherein the causal link module is further configured to determine that the first causal link does not match the second causal link.

7. The system of claim 1 , wherein the causal link module is further configured to:

receive from the prognostic chaining module a third prognosis chained to a third causal link;

evaluate the first causal link and the second causal link and the third causal link; and

select the first causal link by determining that the first causal link matches the second causal link and the second causal link matches the third causal link.

8. The system of claim 7 further comprising:

determining that the first causal link does not match the second causal link and the second causal link matches the third causal link; and

selecting the second causal link.

9. The system of claim 1 , wherein evaluating the first causal link further comprises:

receiving at least an expert input descriptor entered on a graphical user interface operating on the computing device containing a first causal link probability score and a second causal link probability score; and

selecting the first causal link as a function of the expert input descriptor.

10. The system of claim 1 , wherein the degree of similarity further comprises a genetic factor multiplied by an environmental factor multiplied by an inactivity factor.

11. A method of identifying a causal link, the method comprising:

receiving, by a computing device associated with a user, a first user symptom datum from a user client device wherein the first user symptom datum includes a current medical indicator;

receiving by the computing device diagnostic training data from a machine-learning database correlating symptom data to prognostic data;

generating by the computing device, using a supervised machine-learning process, a diagnostic model that receives the first user symptom datum as an input and produces an output containing a first prognosis;

receiving by the computing device an expert input dataset from an expert knowledge database wherein the expert input dataset comprises an expert journal article identifying prognostic data correlated to causal link data;

generating by the computing device a gaussian mixture clustering model utilizing the expert input dataset and the first prognosis and outputting a defined number of clusters;

identifying by the computing device a first causal link chained to the first prognosis as a function of generating the gaussian mixture clustering model associated with the user, wherein the first causal link identifies a root cause of the first prognosis;

receiving by the computing device a second prognosis chained to a second causal link associated with the user, wherein the second causal link identifies a root cause of the second prognosis; and

evaluating by the computing device the first causal link and the second causal link to calculate a degree of similarity between the first causal link and the second causal link, wherein evaluating the first causal link and the second causal link comprises generating a recommendation for further testing as a function of the degree of similarity and displaying the recommendation for further testing at an advisor client device.

12. The method of claim 11 , wherein receiving the first user symptom datum further comprises:

receiving a first user blood test indicating a measure of user genetic data;

receiving genetic training data from a machine-learning database correlating genetic data to prognostic data; and

generating, using a supervised machine-learning process, a genetic model that receives the first user blood test indicating the measure of user genetic data as an input and produces an output containing a first prognosis.

13. The method of claim 11 , wherein generating the gaussian mixture clustering model further comprises:

selecting a defined number of clustering groups;

generating a cluster label for each of the defined number of clustering groups;

assigning the first prognosis to a labeled cluster group; and

identifying a first causal link contained within the labeled cluster group.

14. The method of claim 11 , wherein receiving a second prognosis chained to a second causal link further comprises:

receiving a second user symptom datum from a user client device wherein the second user symptom datum includes a current medical indicator;

receiving diagnostic training data from a machine-learning database correlating symptom data to prognostic data;

generating using a supervised machine-learning process a diagnostic model that receives the second user symptom datum as an input and produces an output containing a second prognosis; and

identifying a second causal link chained to the second prognosis as a function of generating a gaussian mixture clustering model.

15. The method of claim 11 , wherein evaluating the first causal link further comprises selecting the first causal link by determining that the first causal link matches the second causal link.

16. The method of claim 11 , wherein evaluating the first causal link further comprises

determining that the first causal link does not match the second causal link.

17. The method of claim 11 , wherein evaluating the first causal link further comprises:

receiving a third prognosis chained to a third causal link;

evaluating the first causal link and the second causal link and the third causal link; and

selecting the first causal link by determining that the first causal link matches the second causal link and the second causal link matches the third causal link.

18. The method of claim 17 further comprising:

determining that the first causal link does not match the second causal link and the second causal link matches the third causal link; and

selecting the second causal link.

19. The method of claim 11 , wherein evaluating the first causal link further comprises:

receiving at least an expert input descriptor entered on a graphical user interface operating on the computing device containing a first causal link probability score and a second causal link probability score; and

selecting the first causal link as a function of the expert input descriptor.

20. The method of claim 11 , wherein calculating the degree of similarity further comprises a genetic factor multiplied by an environmental factor multiplied by an inactivity factor.